TWI686716B - Text exploration method, computer-readable recording medium and text exploration device recorded with text exploration program - Google Patents

Abstract

In the character analysis step (S109~S110), the hierarchical cluster analysis is performed on the words extracted from the input character data. In the screen generation step (S111), m clusters are obtained from the analysis result of the text analysis step according to the number m of groups and the maximum number n of data in the group, and generated to include the clusters of n or less The group of words is displayed on the screen data of the screen. In the analysis result display step (S112), the screen is displayed based on the generated screen data. In this way, the results of the hierarchical cluster analysis are displayed on the screen in a way that the user can intuitively understand.

Description

Text exploration method, computer-readable recording medium and text exploration device recorded with text exploration program

The invention relates to text exploration, in particular to a text exploration method, a text exploration program, and a text exploration apparatus that display the analysis results of text data on a screen.

In recent years, text exploration that analyzes a large amount of text data recorded in a free form and obtains useful information from the analysis results has attracted attention. In text exploration, for example, words are extracted from the text data of the analysis object, and information is obtained by analyzing the occurrence frequency and trend of the words.

The following is a discussion on a text exploration device that performs hierarchical cluster analysis on the words extracted from the text data and displays the analysis results on the screen. In the hierarchical cluster analysis, based on the similarity between words, hierarchically create clusters containing words with high similarity. In general, the results of the hierarchical cluster analysis are provided to users (analysts) using the tree diagram (dendrogram) shown in FIG. 15.

In connection with the invention of the present application, Patent Document 1 describes a grouping device that has a structured tree diagram, explores the tree diagram, and generates a hierarchical grouping method that can perform a specific index from the lower layer to the upper layer and store it in a storage device. In Reference 2, there is provided a query device, which has: distance matrix calculation means, which calculates the distance between keywords, generates distance matrix data that can explore the distance between keywords and keywords, and stores it in the storage means; and grouping Means, which uses a distance matrix to classify keywords hierarchically The group is stored in the storage means as a bottom-up index that can be explored from the lower level to the upper level of the constructed tree diagram.

[Prior Technical Literature] [Patent Literature]

[Patent Document 1] Japanese Patent Laid-Open No. 2011-216021

[Patent Document 2] Japanese Patent Laid-Open No. 2012-150539

The conventional text exploration device uses a tree diagram to display the results of hierarchical cluster analysis on the screen. However, such a text exploration device has a problem that the user cannot intuitively understand the analysis result. For example, in the analysis result shown in FIG. 15, when the user sets the number of clusters to 4, as shown in FIG. 16, a cutting line is set on the tree diagram. However, users cannot intuitively recognize the words contained in each cluster just by seeing such a tree diagram. In addition, when the number of words is large and the number of clusters is changed, the user cannot intuitively grasp how the words included in each cluster will change.

In addition, because the dendrogram does not record the frequency of occurrence of words, the user cannot know which word is more important. In addition, when the text data of the analysis object is time series data with information such as year, month, day, or time, the user sometimes expects to be able to know the time change of the analysis result. However, the conventional text exploration device cannot meet the above expectations of users.

Therefore, an object of the present invention is to provide a text exploration method, a text exploration program, and a text exploration apparatus that display the results of hierarchical cluster analysis on a screen in a manner that a user can intuitively understand.

The first aspect of the present invention is a text exploration method, which displays the analysis results of text data on the screen, and is characterized by having: a text analysis step for the words (single words) extracted from the input text data , Ie words, word, vocabulary) for hierarchical cluster analysis; screen generation step, which generates screen data based on the analysis results of the above text analysis steps; and analysis result display step, which displays screens based on the screen data; the screen generation Step: Based on the number of groups and the maximum number of data in the group, obtain the cluster of the number of groups from the above analysis results, and generate a group containing the words included in the cluster below the maximum number of data. Picture data of the picture.

The second aspect of the present invention is characterized in that, in the first aspect of the present invention, the words included in the group are selected from the words included in the cluster corresponding to the group, in the order of higher frequency of occurrence .

The third aspect of the present invention is characterized in that, in the second aspect of the present invention, the group has a size corresponding to the total appearance frequency of the words included in the cluster corresponding to the group in the screen.

The fourth aspect of the present invention is characterized in that, in the third aspect of the present invention, the words included in the group are within the screen and have a size corresponding to the frequency of occurrence of the words.

The fifth aspect of the present invention is characterized in that in the first aspect of the present invention, it further includes an instruction input step for inputting an instruction from the user, any of the above-mentioned character analysis step and the above-mentioned screen generation step. It is executed according to the instruction input in the above instruction input step.

The sixth aspect of the present invention is characterized in that, in the fifth aspect of the present invention, the instruction input step receives the setting instruction of the group number, and the screen generation step is based on the group number set in the instruction input step, To generate the above screen data.

The seventh aspect of the present invention is characterized in that, in the fifth aspect of the present invention, the instruction input step receives the setting instruction of the maximum number of data, and the screen generation step is based on the maximum number of data set in the instruction input step, To generate the above screen data.

The eighth aspect of the present invention is characterized in that, in the fifth aspect of the present invention, the instruction input step receives a setting instruction during the analysis target period, and the character analysis step analyzes the analysis set in the instruction input step of the character data The words contained in the text data within the target period are analyzed in the above hierarchical cluster.

The ninth aspect of the present invention is characterized in that, in the fifth aspect of the present invention, the instruction input step receives the setting instruction of the analysis purpose, and the character analysis step extracts from the character data corresponding to the result of the instruction input step. Set the type of analysis purpose words to perform the above hierarchical cluster analysis.

The tenth aspect of the present invention is characterized in that, in the fifth aspect of the present invention, the instruction input step receives a word exclusion instruction, and the character analysis step excludes the word indicated in the instruction input step, and performs the hierarchical form Cluster analysis.

The eleventh aspect of the invention is characterized by the fifth aspect of the invention In the above, the instruction input step receives a synonym registration instruction, and the character analysis step treats the plurality of words indicated in the instruction input step as the same word, and performs the hierarchical cluster analysis.

The twelfth aspect of the present invention is characterized in that, in the fifth aspect of the present invention, the instruction input step receives a compound word registration instruction, and the character analysis step merges the plural words indicated in the instruction input step into a single word And perform the above hierarchical cluster analysis.

The thirteenth aspect of the present invention is characterized in that, in the first aspect of the present invention, the screen generating step generates screen data for displaying the analysis result screen including the group and for setting the above The analysis setting screen of the display form of the analysis result screen.

The fourteenth aspect of the present invention is a computer-readable recording medium that records a text exploration program that displays the analysis results of text data on the screen. It is characterized in that the CPU (Central Processing Unit) uses memory to make the computer execute the following Steps: a text analysis step, which performs hierarchical cluster analysis on the words extracted from the input text data; a screen generation step, which generates screen data based on the analysis results of the above text analysis steps; and an analysis result display step, It displays the screen according to the above-mentioned screen data; the above-mentioned screen generation step obtains the cluster of the above-mentioned group number from the analysis result based on the number of groups and the maximum number of data in the group, and generates a The screen data of the group of words included in the above cluster below the number displayed on the screen.

The fifteenth aspect of the present invention is characterized in that, in the fourteenth aspect of the present invention, the words included in the group are selected from the words included in the cluster corresponding to the group in the order of higher frequency of occurrence .

The sixteenth aspect of the present invention is characterized in that, in the fifteenth aspect of the present invention, the group has a size corresponding to the total appearance frequency of the words included in the cluster corresponding to the group in the screen.

The seventeenth aspect of the present invention is characterized in that in the sixteenth aspect of the present invention, the words included in the group are within the screen and have a size corresponding to the frequency of occurrence of the words.

The eighteenth aspect of the present invention is characterized in that in the fourteenth aspect of the present invention, the computer is further executed to perform any of the instruction input step for inputting instructions from the user, the character analysis step and the screen generation step It is executed according to the instruction input in the instruction input step.

The 19th aspect of the present invention is characterized in that, in the 14th aspect of the present invention, the above-mentioned screen generating step generates screen data for displaying the analysis result screen including the above-mentioned group and for setting the above-mentioned analysis The analysis setting screen of the display aspect of the result screen.

The twentieth aspect of the present invention is a text exploration device that displays the analysis results of text data on the screen, and is characterized by having: a text analysis section that hierarchizes the words extracted from the input text data Cluster analysis; a screen generation unit that generates screen data based on the analysis results of the character analysis unit; and an analysis result display unit that displays screens based on the screen data; the screen generation unit based on the number of groups and within the group The maximum number of data is obtained from the analysis result to obtain the cluster of the number of groups, and generated to display the group containing the word included in the cluster below the maximum number of data on the screen.

The 21st aspect of the present invention is characterized in that, in the 20th aspect of the present invention, the words included in the group are selected from the words included in the cluster corresponding to the group, in the order of higher frequency of occurrence .

The twenty-second aspect of the present invention is characterized in that, in the twenty-first aspect of the present invention, the group has a size corresponding to the total appearance frequency of the words included in the cluster corresponding to the group in the screen.

The twenty-third aspect of the present invention is characterized in that, in the twenty-second aspect of the present invention, the words included in the group are within the screen and have a size corresponding to the appearance frequency of the words.

The 24th aspect of the present invention is characterized in that in the 20th aspect of the present invention, It further includes an instruction input unit for inputting an instruction from the user, and either the character analysis unit or the screen generation unit operates according to the instruction input in the instruction input unit.

The 25th aspect of the present invention is characterized in that, in the 20th aspect of the present invention, the screen generating unit generates screen data for displaying the analysis result screen including the group and for setting the analysis The analysis setting screen of the display aspect of the result screen.

According to the first, 14th, or 20th aspect of the present invention, based on the result of performing hierarchical cluster analysis on the words included in the text data, the group including the words included in the cluster is displayed on the screen. In addition, the number of words included in the group is limited to the maximum number of data. Therefore, the user can intuitively understand the results of the hierarchical cluster analysis when viewing the screen.

According to the second, fifteenth, or twenty-first aspect of the present invention, within the group, words with a high occurrence frequency among words included in the cluster are displayed. Therefore, the user can easily recognize the words with high frequency included in each cluster.

According to the third, 16th, or 22nd aspect of the present invention, the group has a total size within the screen corresponding to the frequency of occurrence of the words included in the cluster. Therefore, the user can easily recognize the cluster with a large total occurrence frequency of the word.

According to the fourth, 17th or 23rd aspect of the present invention, the word has a size corresponding to the word frequency within the screen. Therefore, the user can easily recognize words with a high frequency of occurrence.

According to the fifth, 18th or 24th aspect of the present invention, it can correspond to The user's instruction switches the display of the results of the hierarchical cluster analysis.

According to the sixth aspect of the present invention, the number of groups (the number of clusters) displayed on the screen can be switched according to the instruction from the user.

According to the seventh aspect of the present invention, the upper limit of the number of words included in the group can be switched according to the instruction from the user.

According to the eighth aspect of the present invention, the result of performing hierarchical cluster analysis on the words included in the text data within the analysis target period indicated by the user is displayed on the screen. Therefore, the user can easily recognize the temporal change of the results of the hierarchical cluster analysis.

According to the ninth aspect of the present invention, according to the analysis purpose instructed by the user, the word type of the analysis object can be switched and the results of the hierarchical cluster analysis can be displayed on the screen.

According to the tenth aspect of the present invention, the words indicated by the user can be excluded, and the results of the hierarchical cluster analysis can be displayed on the screen.

According to the eleventh aspect of the present invention, the plurality of words indicated by the user can be regarded as the same word, and the results of the hierarchical cluster analysis can be displayed on the screen.

According to the twelfth aspect of the present invention, the plurality of words indicated by the user can be combined into one word, and the results of the hierarchical cluster analysis can be displayed on the screen.

According to the 13th, 19th or 25th aspect of the present invention, the analysis result screen and the analysis setting screen are displayed. Therefore, the user can use the analysis setting screen to easily switch the display state of the results after the hierarchical cluster analysis.

5‧‧‧ Text

10‧‧‧Text exploration device

11‧‧‧Instruction input section

12‧‧‧ Character Analysis Department

13‧‧‧ Screen generator

14‧‧‧Analysis result display

20‧‧‧ Computer

21‧‧‧CPU

22‧‧‧Main memory

23‧‧‧Storage Department

24‧‧‧ Input

25‧‧‧Display

26‧‧‧Ministry of Communications

27‧‧‧Recording Media Reading Department

28‧‧‧ keyboard

29‧‧‧Mouse

30‧‧‧Recording media

31‧‧‧ Text exploration program

40‧‧‧Display screen

41, 61~68‧‧‧Analysis result screen

42‧‧‧Analysis setting screen

51‧‧‧Data designation screen

52‧‧‧Destination designation screen

53‧‧‧Synonyms list selection screen

54‧‧‧ Compound word list selection screen

m‧‧‧ group number (cluster number)

n‧‧‧Maximum number of data in the group

W1~W6‧‧‧Word

1 is a block diagram showing the structure of a text exploration device according to an embodiment of the present invention.

FIG. 2 is a block diagram showing the structure of a computer that functions as the text exploration device shown in FIG.

FIG. 3 is a diagram showing the display screen of the text exploration device shown in FIG. 1.

4 is a flowchart showing the operation of the text exploration device shown in FIG.

FIG. 5 is a flowchart of screen data generation processing of the text exploration device shown in FIG. 1.

6 is a diagram showing a data designation screen of the text exploration device shown in FIG.

7 is a diagram showing an example of text data input to the text exploration device shown in FIG.

FIG. 8 is a diagram showing the purpose designation screen of the text exploration device shown in FIG. 1.

FIG. 9 is a diagram showing a selection screen of the synonyms list of the text exploration device shown in FIG. 1.

FIG. 10 is a diagram showing a compound word list selection screen of the text exploration device shown in FIG. 1.

11A is a diagram showing an analysis result screen before setting an analysis target period in the text exploration apparatus shown in FIG. 1.

11B is a diagram showing an analysis result screen after setting the analysis target period in the text exploration apparatus shown in FIG. 1.

FIG. 12A is a diagram showing an analysis result screen before word exclusion in the text exploration apparatus shown in FIG. 1.

FIG. 12B is a diagram showing the analysis result screen after the single character is excluded in the text exploration device shown in FIG. 1.

13A is a diagram showing an analysis result screen before registration of synonyms in the text exploration apparatus shown in FIG. 1.

FIG. 13B is a diagram showing an analysis result screen after registration of synonyms in the text exploration apparatus shown in FIG. 1.

14A is a diagram showing an analysis result screen before compound word registration in the text exploration apparatus shown in FIG. 1.

14B is a diagram showing an analysis result screen after compound word registration in the text exploration device shown in FIG. 1.

15 is a diagram showing an example of a tree diagram.

FIG. 16 is a diagram showing a case where the number of clusters is set for the tree diagram shown in FIG. 15.

Figure 17 is a diagram showing the words that appear in the drawings and their descriptions.

The text exploration method, text exploration program, and text exploration apparatus according to the embodiments of the present invention will be described below with reference to the drawings. The text exploration method of this embodiment is usually performed using a computer. The text exploration program in this embodiment is a program for implementing a text exploration method using a computer. The text exploration device of this embodiment is usually constructed using a computer. The computer that runs the text exploration program functions as a text exploration device.

FIG. 1 is a block diagram showing the structure of a text exploration device according to an embodiment of the present invention. The character exploration apparatus 10 shown in FIG. 1 includes an instruction input unit 11, a character analysis unit 12, a screen generation unit 13, and an analysis result display unit 14. Enter the text data 5 of the analysis object in the text exploration device 10. The text exploration device 10 performs hierarchical cluster analysis on the words extracted from the input text data 5 and displays the analysis results on the screen.

The outline of the operation of the text exploration device 10 is as follows. An instruction from the user is input to the instruction input unit 11. The text analysis unit 12 extracts words from the input text data 5 and performs hierarchical cluster analysis on the extracted words. The screen generating unit 13 generates screen data based on the analysis result of the character analysis unit 12. The analysis result display unit 14 displays the screen based on the screen data generated by the screen generation unit 13.

The instruction from the user input to the instruction input unit 11 includes the setting of the number of groups, the setting of the maximum number of data in the group, the setting of the analysis target period, the exception of words, the registration of synonyms, and the registration of compound words. When the text data 5 is time-series data with information such as year, month, day, or time, the text analysis unit 12 compares the input text data 5 with the text data within the analysis target period set by the instruction input unit 11 The included words are analyzed in a hierarchical cluster.

When the screen generating unit 13 generates screen data, it is based on the number of groups and the maximum number of data in the group (details will be described later). In addition, when the user inputs a new instruction, after the indicated processing is performed, the screen generating unit 13 generates new screen data, and the analysis result display unit 14 displays the new screen. In this way, the text exploration device 10 switches the analysis form of the text data 5 and the display form of the analysis result in response to an instruction from the user.

FIG. 2 is a block diagram showing the structure of a computer that functions as a text exploration device 10. The computer 20 shown in FIG. 2 includes a CPU (Central Processing Unit) 21, a main memory 22, a storage unit 23, an input unit 24, a display unit 25, a communication unit 26, and a recording medium reading unit 27 . The main memory 22 uses, for example, DRAM (Dynamic Random Access Memory). The storage unit 23 uses, for example, a hard disk (Hard Disk) or a solid state drive (Solid State Drive). The input unit 24 includes, for example, a keyboard 28 and a mouse (Mouse)29. The display unit 25 uses, for example, a liquid crystal display. The communication unit 26 is an interface circuit for wired communication or wireless communication. The recording medium reading unit 27 is an interface circuit of the recording medium 30 storing programs and the like. For the recording medium 30, for example, CD-ROM (Compact Disc Read-Only Memory; CD-ROM), DVD-ROM (Digital Versatile Disc Read-Only Memory; digital multi-function audio-visual CD-ROM), USB (Universal Serial Bus; (Universal Serial Bus) memory and other non-transitional recording media.

When the computer 20 executes the text exploration program 31, the storage unit 23 stores the text exploration program 31 and the text data 5. The text exploration program 31 and the text data 5 may be, for example, those received from a server or other computer using the communication unit 26, or those read from the recording medium 30 using the recording medium reading unit 27.

When the text exploration program 31 is executed, the text exploration program 31 and the text data 5 are copied and sent to the main memory 22. The CPU 21 uses the main memory 22 as a working memory, and executes the text exploration program 31 stored in the main memory 22 to process the text data 5 stored in the main memory 22. At this time, the computer 20 functions as the text exploration device 10. Furthermore, the configuration of the computer 20 described above is just an example, and any computer can be used to configure the text exploration device 10.

Hereinafter, the text data 5 is set as Japanese data containing Japanese words. Figure 17 is a diagram showing the words appearing in the diagram and its description. Words (Japanese words) and the meaning of words are described in each column of FIG. 17. When referring to Japanese words in the following description, the meaning of the word may sometimes be stated in parentheses after the word. Furthermore, the text data 5 can also be data in any language.

FIG. 3 is a diagram showing the display screen of the text exploration device 10. The display screen 40 shown in FIG. 3 includes an analysis result screen 41 and an analysis setting screen 42. The analysis result of the character analysis unit 12 is displayed on the analysis result screen 41. A GUI (Graphical User Interface) element is displayed on the analysis setting screen 42. The GUI element is used to set the characteristics of the analysis pattern of the character analysis unit 12 and the screen data generated by the screen generation unit 13.

If the number of clusters is set for the result of hierarchical cluster analysis, the words included in each cluster are determined. When the results of hierarchical cluster analysis on the words extracted from the text data 5 are displayed on the screen, the text exploration device 10 displays the group corresponding to the cluster as shown in FIG. 3, instead of the tree diagram .

In the following description, the cluster to be displayed on the screen is also called a cluster. The user uses the instruction input unit 11 to specify the number of groups (the number of clusters) and the maximum number of data in the group (the upper limit of the number of words included in the group). Hereinafter, let the former be m and the latter n.

In the text exploration device 10, the words included in the text data 5 are classified into m clusters, and each cluster contains more than one word. There are m groups displayed on the analysis result screen 41, and words are displayed inside each group. The group is displayed using a cloud-like graphic, and the words included in the group are displayed inside the ellipse area. The number of words included in each group is limited to n or less. For example, when the cluster at n=5 contains 10 words, on the analysis result screen 41, 5 words are displayed inside the group.

On the analysis setting screen 42, the first slide bar for setting the number of groups m and two first buttons (the ones marked with a mark "+" or "-") are displayed, which is used to set the maximum number of data n in the group. The second slider bar and two second buttons, and four boxes and two third buttons (one marked with a left arrow or right arrow) for setting the analysis target period.

The user operates the mouse 29 to move the scroll block of the first slide bar to the left or right or presses the first button to indicate the group number m. The number of groups m will increase when the first button marked with "+" is pressed, and decrease when the first button marked with "-" is pressed. The initial value of the group number m is set to, for example, the square root of the type of word included in the analysis result of the character analysis unit 12, or an integer close to the square root. For example, when the analysis result of the character analysis unit 12 includes 16 kinds of words, the initial value of the number m of groups is set to 4.

The user operates the mouse 29 to move the scrolling block of the second slide bar to the left or right or presses the second button to indicate the maximum number n of data in the group. The maximum number of data n in the group will increase or decrease when the second button is pressed. The initial value of the maximum number of data n in the group is set to 5, for example.

When the text data 5 is time series data, the user uses the keyboard 28 or the mouse 29 to specify the year, month, day, and time using four boxes or presses the third button to indicate the analysis target period. During the analysis, when the third button marked with the left arrow is pressed, it moves towards the past by a certain amount (for example, 1 month), and when the third button marked with the right arrow is pressed, it moves in the opposite direction Both quantitative. The initial value of the analysis target period is, for example, set from the oldest time of the text data 5 to the newest time. Furthermore, when the text data 5 is not time series data, the user cannot specify the analysis target period.

On the analysis result screen 41, there are displayed more than one and m or less groups, and within each group, there are more than one and n or less words. Each group is within the screen, and the greater the total frequency of occurrence of the words included in the corresponding cluster, the larger the display. When the number of words included in the cluster exceeds n, n words with a high frequency are displayed inside the group. The words contained in the group and the ellipses containing them The circled area is displayed with greater magnification as the frequency of words appearing on the screen is higher. Named in each group. The name of the group is the word with the highest frequency among the words contained in the cluster. The name of the group is displayed on the bottom line of the internal label of the group. Furthermore, when a word cannot be displayed inside the ellipse area, the symbol "..." is displayed instead of the word.

On the analysis result screen 41, a third slider bar and two fourth buttons (the ones marked with a symbol "+" or "-") useful for specifying the zoom ratio are displayed. The user sets the zoom ratio by operating the mouse 29 to move the scroll block of the third slide bar to the left or right or pressing the fourth button. On the analysis result screen 41, the group containing the word is displayed enlarged or reduced corresponding to the set zoom ratio. The initial value of the zoom ratio is set to 100%. The analysis result screen 41 in the initial state displays all groups.

When the user changes the number m of groups, the maximum number n of data in the group, or the analysis target period on the analysis setting screen 42, the content of the analysis result screen 41 changes corresponding to these. When the user instructs the analysis result screen 41 to exclude single words, the registration of synonyms, or the registration of compound words, the content of the analysis result screen 41 also changes accordingly.

When performing hierarchical cluster analysis on the words extracted from the text data 5, the text exploration device 10 refers to the list of excluded words that store the words that should be excluded, the list of synonyms that store the words that should be treated as synonyms, and the A list of compound words of words that should be treated as compound words. Plural words with the same meaning (or roughly the same meaning) are associated with one word representing these words and stored in the list of synonyms. If connected, a plurality of words that become a compound word are associated with the compound words connecting the words and stored in the compound word list. For example, "daigakusei (university student)" and "gakusei (student)" and "daigakusei" representing both Correspondence is established and stored in the list of synonyms. For example, "nintai (endurance)" and "tsuyoi (strong)" are linked to the "nintaizuyoi (endurance)" linking the two and stored in the compound word list. The text exploration device 10 may have a plurality of synonym word lists and a plurality of compound word lists.

FIG. 4 is a flowchart showing the operation of the text exploration device 10. FIG. 5 is a flowchart showing the details of the screen data generation process (step S111 shown in FIG. 4) of the text exploration device 10. The input unit 24 and the CPU 21 executing step S113 function as the instruction input unit 11. The CPU 21 that executes steps S109 to S110 functions as the character analysis unit 12. The CPU 21 that executes step S111 functions as the screen generating unit 13. The display unit 25 and the CPU 21 executing step S112 function as the analysis result display unit 14. Hereinafter, the operation of the character exploration apparatus 10 will be described with reference to FIGS. 4 and 5.

First, the CPU 21 causes the display section 25 to display the data designation screen 51 shown in FIG. 6 (step S101). On the data designation screen 51, a box for designating a file name and a box for designating a folder name are displayed. The user specifies the text data 5 of the analysis target by specifying the file name or folder name in the data specifying screen 51. The text data 5 may be stored in a storage unit 23 such as a hard disk, or may be stored in a server or other computer connected to the communication unit 26.

Next, the CPU 21 transmits the text data 5 specified by the use data designation screen 51 to the main memory 22. With this, the text data 5 is input to the text exploration device 10 (step S102). 7 is a diagram showing an example of text data 5. The text data shown in Figure 7 is the data of the report produced by the university students, and is the time series data with the information of the year, month, and day. The text information shown in Figure 7 is in order from the top: "About the relationship between college students and society in the content of this course...", "General college students play before leaving the society after graduation "Work or...", "Our students have to recognize that they are paying expensive tuition to study...", and "Student life is a precious time for self-confidence growth. And...". Furthermore, the type of text data 5 analyzed by the text exploration device 10 is arbitrary.

Next, the CPU 21 causes the display unit 25 to display the destination designation screen 52 shown in FIG. 8 (step S103). On the destination designation screen 52, three radio buttons (Radio Buttons) corresponding to content, features, and evaluation are displayed. The user presses any option button by operating the mouse 29, and selects the analysis purpose from the content, characteristics, and evaluation. Next, the CPU 21 receives the analysis purpose specified in the use purpose specification screen 52. With this, the analysis purpose is input to the text exploration device 10 (step S104).

Next, the CPU 21 causes the display unit 25 to display the synonym list selection screen 53 shown in FIG. 9 (step S105). On the synonym list selection screen 53, the names of the synonym lists included in the text exploration device 10 and the synonym words registered in each synonym list are displayed. The user selects any synonym list in the synonym list selection screen 53 by operating the mouse 29 to specify the synonym list to be used. With this, the synonym word list is selected in the character exploration device 10 (step S106).

Next, the CPU 21 causes the display unit 25 to display the compound word list selection screen 54 shown in FIG. 10 (step S107). The compound word list selection screen 54 displays the name of the compound word list included in the text exploration device 10 and the compound word registered in each compound word list. The user selects any compound word list on the compound word list selection screen 54 by operating the mouse 29 to specify the compound word list to be used. With this, the compound word list is selected in the character exploration device 10 (step S108).

(SA)行變格活用名詞、及動詞。CPU 21在分析目的為「評價」之情形時，自文字資料5擷取形容詞、形容動詞、及感嘆詞。再者，文字探勘裝置10亦可支援前述之3個以外之分析目的。又，CPU 21亦可根據各分析目的而擷取與前述不同種類之單字。 Next, the CPU 21 considers the exclusion word list, the synonym list, and the compound word list, and the text data belonging to the analysis target period in the text data 5 input in step S102, extracts the type corresponding to the analysis purpose specified in step S104 Word (step S109). The CPU 21 extracts nouns, proper nouns, place names, and person names from the text data 5 when analyzing the case where the purpose is "content". When the analysis purpose is "characteristic", the CPU 21 extracts nouns, proper nouns,

(SA) The use of nouns and verbs. The CPU 21 extracts adjectives, adjective verbs, and interjections from the text data 5 when the analysis purpose is "evaluation". In addition, the text exploration device 10 can also support the above three analysis purposes. In addition, the CPU 21 can also extract different kinds of words according to each analysis purpose.

When the text data 5 is time-series data, the CPU 21 executes step S109 to extract only words from the text data included in the analysis target period indicated by the user in the text data 5. In addition, when the word W1 is stored in the excluded word list, the CPU 21 completely ignores the word W1 included in the text data 5 when executing step S109. In addition, when the word W2 and the word W3 are associated with the word W2 representing the two and are stored in the selected synonym list, when the CPU 21 executes step S109, all the words W3 included in the text data 5 are included Treated as a word W2. In addition, when the word W4 and the word W5 are associated with the word W6 connecting the two and stored in the selected compound word list, the CPU 21 executes step S109 and converts the connected word included in the text data 5 W4 and word W5 are all treated as word W6.

Next, the CPU 21 performs hierarchical cluster analysis on the words extracted in step S109 (step S110). In step S110, the CPU 21 obtains the similarity between the two words according to the distance between the two words in the text data 5 (how far apart the two words are separated). The CPU 21 uses a predetermined method (for example, the shortest distance method, the longest distance method, the group average method, the decimal method, the Ward's method, etc.) to perform hierarchical clustering according to the similarity between the obtained words analysis. also, In step S110, the CPU 21 obtains the appearance frequency of each word.

Next, the CPU 21 generates screen data for displaying the analysis result based on the result of the hierarchical cluster analysis obtained in step S110 (step S111). The CPU 21 performs the processing shown in FIG. 5 in step S111.

The CPU 21 sets the number of groups to m, and sets the maximum number of data in the group to n (step S201). Next, the CPU 21 sets the number of clusters to m based on the result of hierarchical cluster analysis to obtain m clusters (step S202). Next, the CPU 21 obtains the total frequency of occurrence of the words included in the cluster for each cluster (step S203). Next, the CPU 21 determines the display size of each group based on the total of the appearance frequencies obtained in step S203 (step S204). In step S204, the greater the total frequency of occurrence of the words included in the cluster, the larger the display size of the group is determined.

Next, for each cluster, the CPU 21 selects the word to be displayed from the words included in the cluster (step S205). In step S205, from the words included in each cluster, n words or less are selected in the order of high frequency of occurrence. Next, for each word selected in step S205, the CPU 21 determines the display size of the word based on the frequency of occurrence of the word (step S206). In step S206, the higher the word frequency, the larger the display size of the word.

Next, the CPU 21 generates screen data for displaying the results of the hierarchical cluster analysis (step S207). The picture data generated in step S207 includes m groups (represented by a cloud-like figure) having the size determined in step S204. Within each group, there are n words or less with the size determined in step S206. Words are displayed on the screen and displayed inside the group. After executing step S207, the CPU 21 ends the screen material generation process.

Next, the CPU 21 causes the display unit 25 to display a screen based on the screen data generated in step S111 (step S112). Next, the CPU 21 receives an instruction from the user (step S113). Next, the CPU 21 proceeds to any one of steps S115 to S120 according to the type of instruction received in step S113 (step S114).

The CPU 21 proceeds to step S115 when the instruction received in step S113 is "setting of group number". In this case, the CPU 21 sets the group number m to the value indicated by the user (step S115), and proceeds to step S111. After that, the screen data is generated according to the set number of groups m, and a new screen is displayed. By this, the analysis result screen containing the specified number of groups is displayed.

The CPU 21 proceeds to step S116 when the instruction received in step S113 is "setting of the maximum number of data in the group". In this case, the CPU 21 sets the maximum number n of data in the group to the value indicated by the user (step S116), and proceeds to step S111. After that, the screen data is generated according to the set maximum number of data n in the group, and a new screen is displayed. With this, the analysis result screen in which the number of words included in each group is limited to the specified value is displayed.

When the instruction received in step S113 is "setting of analysis target period", the CPU 21 proceeds to step S117. In this case, the CPU 21 sets the analysis target period to the period instructed by the user (step S117), and proceeds to step S109. Thereafter, a hierarchical cluster analysis is performed with reference to the set analysis target period, and screen data for displaying new analysis results is generated, and a new screen is displayed. In this way, the results of the hierarchical cluster analysis of the words included in the text data within the specified analysis target period are displayed on the screen.

FIG. 11A is a diagram showing an analysis result screen before setting an analysis target period. 11B is a diagram showing an analysis result screen after setting an analysis target period. in The analysis result screen 61 before setting shown in FIG. 11A displays the text data of the input text data 5 from 0:00 on January 1, 2014 to 24:00 on December 31, 2015 The included words are the result of hierarchical cluster analysis. The analysis result screen 62 after the setting shown in FIG. 11B displays the text data of the input text data 5 from 00:00 on March 1, 2014 to 24:00 on September 30, 2014 The included words are the result of hierarchical cluster analysis. The display content of the analysis result screen 61 is different from the display content of the analysis result screen 62. The user can easily recognize the temporal change of the hierarchical cluster analysis results by observing the analysis result screens before and after the set analysis target period.

The CPU 21 proceeds to step S118 when the instruction received in step S113 is "except for single word". In this case, the CPU 21 adds the specified word to the excluded word list (step S118), and proceeds to step S109. After that, except for the specified words, a hierarchical cluster analysis is performed to generate screen data for displaying new analysis results, and display the new screen. With this, the result of performing hierarchical cluster analysis except for the specified word is displayed on the screen.

Fig. 12A is a diagram showing the analysis result screen before the word exclusion. Fig. 12B is a diagram showing an analysis result screen after excluding single words. The user operates the mouse 29, and after selecting the word to be excluded, instructs to exclude the word. On the analysis result screen 63 before the exclusion of the words shown in FIG. 12A, select "shakai (society)" and select "exclude words" from the menu. After that, the results of hierarchical cluster analysis except "shakai" are displayed on the screen. In the analysis result screen 64 after excluding the words shown in FIG. 12B, "shingaku" is displayed instead of "shakai". Among the words included in the same cluster as "shakai", "shingaku" is the second word after the analysis result screen 63, with the highest frequency.

The CPU 21 proceeds to step S119 when the instruction received in step S113 is "synonym registration". In this case, the CPU 21 adds the indicated word to the list of synonyms in use (step S119), and proceeds to step S109. After that, the hierarchical cluster analysis is performed considering the indicated synonyms, and the screen data for displaying the new analysis result is generated, and the new screen is displayed. In this way, the result of performing hierarchical cluster analysis using the indicated word as a synonym is displayed on the screen.

FIG. 13A is a diagram showing a screen of analysis results before registration of synonyms. FIG. 13B is a diagram showing an analysis result screen after registration of synonyms. The user operates the mouse 29, and after selecting a plurality of words that should be registered as synonyms, instructs to register the synonyms. On the analysis result screen 65 before the registration of synonyms, as shown in FIG. 13A, select "daigakusei (university student)" and "gakusei (student)", and select "synonym registration" from the menu. After that, the results of hierarchical cluster analysis using "daigakusei" and "gakusei" as synonyms are displayed on the screen. In the analysis result screen 66 after the registration of the synonym shown in FIG. 13B, "daigakusei" is displayed in a larger size than the analysis result screen 65, and "shingaku" is displayed instead of "gakusei". Based on the sum of the appearance frequency of "daigakusei" and the appearance frequency of "gakusei", "daigakusei" is displayed in a larger size than "daigakusei" in the analysis result screen 65.

When the instruction received in step S113 is "composite word registration", the CPU 21 proceeds to step S120. In this case, the CPU 21 adds the indicated word to the compound word list in use (step S120), and proceeds to step S109. After that, the hierarchical cluster analysis is performed considering the indicated compound words, and the screen data for displaying the new analysis result is generated, and the new screen is displayed. With this, the result of performing hierarchical cluster analysis using the specified word as a compound word is displayed on the screen.

FIG. 14A is a diagram showing an analysis result screen before compound word registration. 14B is a diagram showing the analysis result screen after compound word registration. The user operates the mouse 29 to select a plurality of words that should be registered as compound words, and then instructs to perform "synonym registration". In the analysis result screen 67 before compound word registration shown in FIG. 14A, "nintai (endurance)" and "tsuyoi (strong)" are selected, and "compound word registration" is selected from the menu. After that, the hierarchical cluster analysis using "nintai" and "tsuyoi" as compound words is displayed on the screen. In the analysis result screen 68 after registration of the compound word shown in FIG. 14B, instead of "nintai" and "tsuyoi", "nintaizuyoi (high endurance)" is displayed at a size below "nintai" and "tsuyoi".

As shown above, the text exploration method of this embodiment includes: a text analysis step, which performs hierarchical cluster analysis on the words extracted from the input text data; a screen generation step, which is based on the analysis result of the text analysis step , Generate screen data; and analysis result display step, which displays the screen according to the screen data. The screen generation step is to obtain m clusters from the analysis result based on the number of groups m and the maximum number of data in the group n, and generate a group for displaying the words contained in n or less clusters on the screen Screen data. According to the text exploration method of this embodiment, the group containing the words included in the cluster can be displayed on the screen based on the result of hierarchical cluster analysis of the words included in the text data. In addition, the number of words included in the group is limited to n or less. Therefore, the user can intuitively understand the results of hierarchical cluster analysis when seeing the screen.

In addition, the words included in the group are selected from the words included in the cluster corresponding to the group, in the order of high frequency of occurrence. Therefore, within the group, words with a high frequency appearing among the words included in the cluster are displayed. Therefore, the user can easily Understand the high-frequency words included in each cluster. In addition, the group has a size corresponding to the total appearance frequency of the words included in the cluster corresponding to the group within the screen. Therefore, the user can easily recognize a cluster with a larger total frequency of occurrence of words. In addition, the words included in the group have a size corresponding to the frequency of occurrence of the words within the screen. Therefore, the user can easily recognize words with a high frequency of occurrence.

In addition, the text exploration method has an instruction input step useful for inputting instructions from the user, and any one of the character analysis step and the screen generation step is executed according to the instruction input in the instruction input step. Therefore, according to the instruction from the user, the display of the results of the hierarchical cluster analysis can be switched. In particular, the instruction input step receives a setting instruction of the group number m, and the screen generation step generates screen data according to the group number m specified in the instruction input step. With this, according to the instruction from the user, the number of areas (the number of clusters) displayed on the screen is switched. In addition, the instruction input step receives the maximum number n of data in the group, and the screen generation step generates screen data based on the maximum number n of data in the group specified in the instruction input step. Thereby, according to the instruction from the user, the number of words displayed in the area is switched.

In addition, the instruction input step receives the instruction of the analysis target period, and the character analysis step performs hierarchical cluster analysis on the words included in the character data within the analysis target period specified by the instruction input step in the text data. Therefore, the result of performing hierarchical cluster analysis on the words included in the text data within the analysis target period indicated by the user is displayed on the screen. Therefore, the user can easily recognize the temporal change of the results of the hierarchical cluster analysis. In addition, the instruction input step receives the setting instruction of the analysis purpose, and the text analysis step extracts words corresponding to the type of analysis purpose set in the instruction input step from the text data 5 to perform hierarchical cluster analysis. In this way, the word type of the analysis object can be switched according to the analysis purpose indicated by the user, and the The results of the hierarchical cluster analysis are displayed on the screen.

In addition, the instruction input step receives the word exclusion instruction, and the character analysis step excludes the word indicated in the instruction input step, and performs hierarchical cluster analysis. In this way, the words indicated by the user can be excluded and the results of hierarchical cluster analysis can be displayed. In addition, the instruction input step receives a synonym registration instruction, and the character analysis step treats the plural words indicated in the instruction input step as the same word, and performs hierarchical cluster analysis. In this way, the plurality of words indicated by the user can be regarded as the same word and the results of the hierarchical cluster analysis can be displayed on the screen. In addition, the instruction input step receives the compound word registration instruction, and the character analysis step combines the plural words indicated in the instruction input step into one word to perform hierarchical cluster analysis. In this way, the plural words indicated by the user can be combined into one word, and the results of the hierarchical cluster analysis can be displayed on the screen.

In addition, the screen generation step generates screen data for displaying the analysis result screen including the group and the analysis setting screen for setting the display appearance of the analysis result screen. Therefore, the analysis result screen and the analysis setting screen are displayed. Therefore, the user can easily switch the display state of the results of the hierarchical cluster analysis using the analysis setting screen.

The text exploration program 31 of this embodiment and the text exploration apparatus 10 of this embodiment have the same configuration as the text exploration processing method of this embodiment, and exert the same effects.

According to the text exploration method, text exploration program, and text exploration device of this embodiment, the group of words included in the cluster with the maximum number of data can be made based on the results of hierarchical cluster analysis of the words included in the text data Is displayed on the screen. Therefore, the user can intuitively understand the hierarchical cluster when seeing the screen The result of the analysis.

Furthermore, this case claims to apply for priority based on Japanese Patent Application No. 2016-145065 with the invention titled "Text Exploration Method, Text Exploration Program, and Text Exploration Device" filed on July 25, 2016. The content of these applications is included in this application by reference.

40‧‧‧Display screen

41‧‧‧Analysis result screen

42‧‧‧Analysis setting screen

Claims (25)

A text exploration method that displays the analysis results of text data on the screen; it is characterized by the following: a text analysis step, which performs hierarchical cluster analysis on the words extracted from the input text data; screen generation Step, which generates screen data based on the analysis results of the above text analysis step; and analysis result display step, which displays the screen based on the above screen data; the above screen generation step is based on the number of groups and the maximum number of data in the group, from the above The analysis result finds the clusters of the above-mentioned group number, and generates screen data for displaying the group containing the words included in the cluster below the maximum number of data on the screen, and the group contains the cluster The word with the highest frequency among words is used as the name. For example, in the text exploration method of claim 1, the words included in the group are selected from the words included in the cluster corresponding to the group in the order of high frequency of occurrence. As in the text exploration method of claim 2, wherein the group has a size corresponding to the total frequency of occurrence of words included in the cluster corresponding to the group within the screen. For example, in the text exploration method of claim 3, the words included in the group are within the screen and have a size corresponding to the frequency of occurrence of the words. For example, the text exploration method of claim 1, which further includes an instruction input step for inputting instructions from the user. Any of the above text analysis step and the above screen generation step is based on the above The instruction input in the instruction input step is executed. For example, the text exploration method of claim 5, wherein the instruction input step receives the setting instruction of the group number, and the screen generation step generates the screen data according to the group number set in the instruction input step. As in the text exploration method of claim 5, wherein the instruction input step receives the setting instruction of the maximum number of data, the screen generation step generates the screen data according to the maximum number of data set in the instruction input step. As in the text exploration method of claim 5, wherein the instruction input step receives the setting instruction during the analysis object period, the character analysis step includes the text data included in the text data in the analysis object period set in the instruction input step in the text data Single word, perform the above hierarchical cluster analysis. As in the text exploration method of claim 5, wherein the instruction input step receives the setting instruction of the analysis purpose, the character analysis step extracts words corresponding to the type of analysis purpose set in the instruction input step from the text data, to Perform the above hierarchical cluster analysis. For example, in the text exploration method of claim 5, wherein the instruction input step receives a word exclusion instruction, the character analysis step will exclude the word indicated in the instruction input step, and perform the hierarchical cluster analysis. As in the text exploration method of claim 5, wherein the above instruction input step is received In the synonym registration instruction, the character analysis step treats the plural words indicated in the instruction input step as the same word, and performs the hierarchical cluster analysis. As in the text exploration method of claim 5, wherein the instruction input step receives a compound word registration instruction, the character analysis step combines the plural words indicated in the instruction input step into a single word, and performs the hierarchical cluster analysis. As in the text exploration method of claim 1, wherein the above-mentioned screen generation step generates screen data, the screen data is used to display the analysis result screen including the above-mentioned group, and the analysis setting to set the display appearance of the analysis result screen Screen. A computer-readable recording medium that records a text exploration program that displays the analysis results of text data on the screen, characterized in that the CPU uses memory to cause the computer to perform the following steps: text analysis step, which is self-entered Words extracted from the text data are subjected to hierarchical cluster analysis; a screen generation step, which generates screen data based on the analysis results of the text analysis step; and an analysis result display step, which displays the screen based on the screen data; the screen The generating step obtains the cluster of the above group number from the analysis result based on the number of groups and the maximum number of data in the group, and generates a group display for displaying the words included in the cluster below the maximum number of data In the picture data of the picture, the word with the highest frequency among the words included in the cluster is added to the group as the name. For example, the computer-readable recording medium of item 14, wherein the above-mentioned group includes The included words are selected from the words included in the cluster corresponding to the above group in the order of high frequency of occurrence. The computer-readable recording medium of claim 15, wherein the group is within the screen and has a size corresponding to the total frequency of occurrence of words included in the cluster corresponding to the group. For example, the computer-readable recording medium of claim 16, wherein the words included in the group are within the screen and have a size corresponding to the frequency of occurrence of the words. For example, the computer-readable recording medium of claim 14, wherein the computer is further subjected to an instruction input step for inputting an instruction from a user, any of the text analysis step and the screen generation step are based on The instruction input in the instruction input step described above is executed. For example, the computer-readable recording medium of claim 14, wherein the screen generation step generates screen data that is used to display the analysis result screen including the group and to set the display state of the analysis result screen This kind of analysis setting screen. A text exploration device that displays the analysis results of text data on a screen. It is characterized in that it includes: a text analysis section that performs hierarchical cluster analysis on the words extracted from the input text data; screen generation Part, which generates screen data based on the analysis result of the character analysis part; and analysis result display part, which displays the screen based on the screen data; the screen generation part based on the number of groups and the maximum number of data The result of the above analysis finds the cluster with the above number of groups, and generates a The screen data of the group of words included in the above cluster is displayed on the screen, and the word with the highest frequency among the words included in the cluster is attached to the group as the name. For example, in the text exploration device of claim 20, the words included in the group are selected from the words included in the cluster corresponding to the group in the order of high frequency of occurrence. The text exploration apparatus according to claim 21, wherein the group has a size corresponding to the total frequency of occurrence of words included in the cluster corresponding to the group within the screen. A text exploration device according to claim 22, wherein the words included in the group are within the screen and have a size corresponding to the frequency of occurrence of the words. The text exploration device according to claim 20, further comprising an instruction input unit for inputting an instruction from the user, any one of the character analysis unit and the screen generation unit according to the instruction input in the instruction input unit Come action. A text exploration apparatus according to claim 20, wherein the screen generating unit generates screen data for displaying the analysis result screen including the group and the analysis setting for setting the display appearance of the analysis result screen Screen.

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