KR102175658B1 - Text mining method, text mining program and text mining apparatus - Google Patents

Abstract

In the text mining method, a step of extracting words from text data consisting of doors with dates, a step of performing a hierarchical cluster analysis on the extracted words for each analysis period, and a screen including the results of the hierarchical cluster analysis are displayed. It has a step to do. When an instruction for designating a target word is input in the first screen including the analysis result, the cluster name based on the word included in the cluster including the target word is displayed along the time axis, A second screen showing changes over time is displayed. This makes it possible to easily recognize the change over time in the hierarchical cluster analysis result.

Description

Text mining method, text mining program, and text mining device {TEXT MINING METHOD, TEXT MINING PROGRAM AND TEXT MINING APPARATUS}

The present invention relates to text mining, and more particularly, to a text mining method, a text mining program, and a text mining apparatus for displaying a screen including a result of a hierarchical cluster analysis.

Recently, text mining, which analyzes freely described text data and obtains useful information from the analysis results, has attracted attention. In text mining, for example, information is obtained by extracting words from text data to be analyzed, and analyzing the frequency and tendency of the words to appear.

Hereinafter, a text mining apparatus that performs hierarchical cluster analysis on words extracted from text data and displays a screen including the results will be considered. In hierarchical cluster analysis, clusters including words with high similarity are hierarchically created based on the similarity between words. In general, the results of hierarchical cluster analysis are provided to an analyst using a tree diagram (dendogram) shown in FIG. 10. The analyst can grasp the outline of the text data based on the result of the hierarchical cluster analysis.

Japanese Patent Application Laid-Open No. 2018-18118 discloses a text mining apparatus that displays the results of hierarchical cluster analysis in the manner shown in FIG. 11. The text mining apparatus described in this document obtains m clusters from the results of hierarchical cluster analysis when the number of clusters m and the maximum number of displayed data n in the cluster is given, and displays the obtained m clusters as a cloud-shaped figure. And, n or fewer words are displayed inside each cluster.

Among the text data, there are some which consist of documents with dates, such as records of maintenance work or records of telephone responses from a call center, and are accumulated cumulatively over a long period of time. When performing a hierarchical cluster analysis on such text data, the text data is divided by month, for example, and a hierarchical cluster analysis is performed on the text data of each month. Accordingly, the results of the hierarchical cluster analysis can be obtained monthly.

In this case, the analyst selects a word to pay attention to (hereinafter referred to as a attention word) from the text data, and in each month, the cluster containing the attention word, the time when the cluster including the attention word changes, the appearance of the attention word. I want to know the change in frequency over time. However, in the conventional text mining apparatus, the user cannot easily recognize the change over time in the hierarchical cluster analysis result.

Therefore, an object of the present invention is to provide a text mining method, a text mining program, and a text mining apparatus in which a user can easily recognize a change over time in a hierarchical cluster analysis result.

A first aspect of the present invention is a text mining method for displaying a screen including an analysis result of text data,

A step of extracting a word from text data consisting of a statement having a date,

A step of performing a hierarchical cluster analysis on the word for each analysis period,

Comprising a step of displaying a screen including a result of the hierarchical cluster analysis,

When an instruction for designating a target word is input within the first screen including the result, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word. It features.

In the second aspect of the present invention, in the first aspect of the present invention,

The second screen is characterized in that a cluster name based on words included in the cluster is displayed along a time axis.

The third aspect of the present invention, in the second aspect of the present invention,

The cluster name is characterized in that the words included in the cluster are concatenated by a predetermined number or less in the order of their appearance frequency.

In the fourth aspect of the present invention, in the second aspect of the present invention,

The second screen is characterized in that it further includes a mark having an aspect according to a degree of change of the cluster name at a position corresponding to a time when the cluster name changes.

In the fifth aspect of the present invention, in the fourth aspect of the present invention,

The mark is characterized in that it is an arrow having a color according to the degree of change of the cluster name.

In the sixth aspect of the present invention, in the second aspect of the present invention,

Among words constituting the cluster name, a word that has changed from the previous cluster name is highlighted in the second screen.

In the seventh aspect of the present invention, in the second aspect of the present invention,

The second screen is characterized in that it further includes a graph showing a change over time in the frequency of appearance of the main word along the time axis.

In the eighth aspect of the present invention, in the seventh aspect of the present invention,

The second screen is characterized in that a boundary line is additionally included at a position corresponding to a time when the cluster name changes, and the background of the graph has a different aspect for each boundary line.

In the ninth aspect of the present invention, in the second aspect of the present invention,

When the cluster name frequently changes greatly, the step of displaying the screen is characterized in that a screen including a warning message is displayed.

A tenth aspect of the present invention is a text mining program for displaying a screen including an analysis result of text data,

A step of extracting a word from text data consisting of a statement having a date,

A step of performing a hierarchical cluster analysis on the word for each analysis period,

The CPU executes the step of displaying a screen including the result of the hierarchical cluster analysis in the computer using a memory,

When an instruction for designating a target word is input within the first screen including the result, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word. It features.

In the eleventh aspect of the present invention, in the tenth aspect of the present invention,

The second screen is characterized in that a cluster name based on words included in the cluster is displayed along a time axis.

In the twelfth aspect of the present invention, in the eleventh aspect of the present invention,

The cluster name is characterized in that the words included in the cluster are concatenated by a predetermined number or less in the order of their appearance frequency.

In the thirteenth aspect of the present invention, in the eleventh aspect of the present invention,

The second screen is characterized in that it further includes a mark having an aspect according to a degree of change of the cluster name at a position corresponding to a time when the cluster name changes.

In the fourteenth aspect of the present invention, in the thirteenth aspect of the present invention,

The mark is characterized in that it is an arrow having a color according to the degree of change of the cluster name.

In the fifteenth aspect of the present invention, in the eleventh aspect of the present invention,

Among words constituting the cluster name, a word that has changed from the previous cluster name is highlighted in the second screen.

In the sixteenth aspect of the present invention, in the eleventh aspect of the present invention,

The second screen is characterized in that it further includes a graph showing a change over time in the frequency of appearance of the main word along the time axis.

In the seventeenth aspect of the present invention, in the sixteenth aspect of the present invention,

The second screen is characterized in that a boundary line is additionally included at a position corresponding to a time when the cluster name changes, and the background of the graph has a different aspect for each boundary line.

In the eighteenth aspect of the present invention, in the eleventh aspect of the present invention,

When the cluster name frequently changes greatly, the step of displaying the screen is characterized in that a screen including a warning message is displayed.

A nineteenth aspect of the present invention is a text mining apparatus that displays a screen including an analysis result of text data,

A word extraction unit for extracting a word from text data consisting of a statement having a date,

A clustering processing unit that performs hierarchical cluster analysis on the word by analysis period,

A screen display unit for displaying a screen including a result of the hierarchical cluster analysis,

When an instruction for designating a target word is input in the first screen including the result, the screen display unit displays a second screen indicating changes over time of the cluster including the target word. do.

In the twentieth aspect of the present invention, in the nineteenth aspect of the present invention,

The second screen is characterized in that a cluster name based on words included in the cluster is displayed along a time axis.

According to the first, tenth, or nineteenth phase, when an instruction for designating a target word is input in the first screen including the result of hierarchical cluster analysis, the change over time of the cluster including the target word By displaying the second screen indicating Δ, the user can easily recognize the change over time in the hierarchical cluster analysis result.

According to the second, eleventh, or twentieth phase, the cluster name based on the words included in the cluster including the target word is represented along the time axis, so that the user can change the change of the cluster including the target word over time. It can be easily recognized.

According to the third or twelfth aspect, the cluster name in which words with high frequency appearing in the cluster including the target word are connected along the time axis is displayed, so that the user can change the change over time of the cluster including the target word. It can be easily recognized.

According to the fourth, fifth, thirteenth or fourteenth aspect, a second screen including a mark having a mode according to the degree of change of the cluster name including the main word (arrow with a color according to the degree of change) By displaying, the user can easily recognize the degree of change in the cluster including the target word.

According to the sixth or fifteenth aspect, by highlighting the changed word among the words constituting the name of the cluster including the target word, the user can see how the word with high frequency of appearance in the cluster including the target word has changed. It can be easily recognized.

According to the seventh or sixteenth aspect, by displaying a screen including a graph indicating changes over time in the frequency of appearance of the attention word, in addition to the change over time of the cluster including the attention word, the user Changes in the hierarchical cluster analysis result over time can be easily recognized.

According to the eighth or seventeenth aspect, by displaying a boundary line at a position corresponding to the time when the name of the cluster containing the attention word changes, and changing the mode of the graph background for each boundary line, the user includes the attention word. It is easy to recognize when the cluster changes.

According to the ninth or eighteenth aspect, by displaying a screen including a warning message when the name of the cluster containing the target word is frequently changed, the user can recognize that hierarchical cluster analysis is not working well. I can.

1 is a block diagram showing the configuration of a text mining apparatus according to an embodiment of the present invention.
Fig. 2 is a block diagram showing the configuration of a computer operating as the text mining apparatus shown in Fig. 1;
3 is a flow chart showing the operation of the text mining device shown in FIG. 1.
FIG. 4 is a diagram illustrating an example of a window showing a result of hierarchical cluster analysis displayed by the text mining device shown in FIG. 1.
5 is a diagram showing an operation for designating a target word within the window shown in FIG. 4.
6 is a diagram illustrating an example of a window showing changes over time in an analysis result displayed by the text mining apparatus shown in FIG. 1.
7 is a diagram illustrating an example of a display screen of the text mining device shown in FIG. 1.
8A is a diagram illustrating an example of changes over time in a hierarchical cluster analysis result.
Fig. 8B is a view following Fig. 8A.
Fig. 8C is a diagram following Fig. 8B.
Fig. 8D is a diagram following Fig. 8C.
9 is a diagram showing a window displayed by the text mining device shown in FIG. 1;
10 is a diagram showing an example of a vertical diagram.
11 is a diagram showing a display mode of hierarchical cluster analysis results in a conventional text mining apparatus.

Hereinafter, a text mining method, a text mining program, and a text mining apparatus according to an embodiment of the present invention will be described with reference to the drawings. The text mining method according to this embodiment is typically executed using a computer. The text mining program according to the present embodiment is a program for implementing a text mining method using a computer. The text mining apparatus according to the present embodiment is typically configured using a computer. A computer executing a text mining program functions as a text mining device.

1 is a block diagram showing a configuration of a text mining apparatus according to an embodiment of the present invention. The text mining device 10 shown in Fig. 1 includes an instruction input unit 11, a text data storage unit 12, a word extraction unit 13, a clustering processing unit 14, an analysis result storage unit 15, and a screen display unit. (16) is provided. The text mining device 10 performs hierarchical cluster analysis on the text data stored in the text data storage unit 12, and displays a screen including the analysis result.

The outline of the operation of the text mining device 10 is as follows. In the instruction input unit 11, an instruction from a user (analyzer of text data) is input. The text data storage unit 12 stores one or more freely described text data. The word extraction unit 13 extracts words from the text data by performing morpheme analysis on the text data stored in the text data storage unit 12. The clustering processing unit 14 performs hierarchical cluster analysis on the words extracted by the word extraction unit 13. The analysis result storage unit 15 stores the analysis result by the clustering processing unit 14. The screen display unit 16 displays screen data based on the analysis result stored in the analysis result storage unit 15.

The text data storage unit 12 is made of a door having a date and stores text data accumulated over a long period of time (for example, several years). Using the instruction input unit 11, the user inputs text data to be analyzed, an instruction to designate an analysis period and an analysis interval, an instruction to designate an attention word, and the like. The word extracting unit 13, the clustering processing unit 14, and the screen display unit 16 perform an operation for displaying a screen including the result of performing hierarchical cluster analysis on text data according to an instruction from the user. do. In addition, the screen display unit 16 displays a screen including changes over time in the hierarchical cluster analysis result according to an instruction from the user.

2 is a block diagram showing the configuration of a computer functioning as the text mining device 10. The computer 20 shown in FIG. 2 includes a CPU 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. It is equipped with. For the main memory 22, for example, DRAM is used. For the storage unit 23, for example, a hard disk or a solid state drive is used. The input unit 24 includes, for example, a keyboard 28 and a mouse 29. For the display portion 25, for example, a liquid crystal display is used. 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, a non-transitory recording medium such as a CD-ROM, a DVD-ROM, or a USB memory is used.

When the computer 20 executes the text mining program 31, the storage unit 23 stores the text mining program 31 and text data 32. The text mining program 31 and the text data 32 may be received from, for example, a server or another computer using the communication unit 26, or the recording medium reading unit 27 from the recording medium 30. It may be used and read out.

When executing the text mining program 31, the text mining program 31 and text data 32 are copied and transferred to the main memory 22. The CPU 21 uses the main memory 22 as a working memory and executes the text mining program 31 stored in the main memory 22, thereby extracting a word from the text data 32, the extracted word A process of performing hierarchical cluster analysis and a process of displaying a screen including analysis results are performed. At this time, the computer 20 functions as the text mining device 10. In addition, the configuration of the computer 20 described above is only an example, and the text mining apparatus 10 can be configured using any computer.

3 is a flow chart showing the operation of the text mining device 10. Before performing the operation shown in FIG. 3, the text data storage unit 12 stores one or more text data that is freely described and accumulated cumulatively. Text data consists of a statement with a date (eg, work day or reception date, etc.), and text data is divided into a plurality of parts by date. The text mining device 10 performs processing on text data designated by a user from among text data stored in the text data storage unit 12.

In Fig. 3, the instruction input unit 11 first receives an instruction to designate the text data to be analyzed, the analysis period, and the analysis interval from the user (step S101). The user uses the input unit 24 to input these information into a dialog box (not shown) displayed on the screen. The received instruction is output to each unit of the text mining device 10.

Next, the word extracting unit 13 reads and outputs the designated text data from the text data storage unit 12 (step S102). Next, the word extraction unit 13 extracts a word from the read-out text data by performing morpheme analysis on the text data read and output in step S102 (step S103). At this time, the word extracting unit 13 extracts only words necessary for subsequent analysis from the read-out text data.

Next, the clustering processing unit 14 performs hierarchical cluster analysis on the words extracted in step S103 (step S104). Next, the clustering processing unit 14 calculates the frequency of appearance of the word extracted in step S103 (step S105). Next, the analysis result storage unit 15 stores the hierarchical cluster analysis result obtained in step S104 and the frequency of occurrence of the word determined in step S105 (step S106).

The clustering processing unit 14 receives, from the instruction input unit 11, an analysis period and an analysis interval designated by the user. The analysis period indicates a period during which hierarchical cluster analysis is actually performed among text data to be analyzed. The analysis period is divided into a plurality of periods based on the analysis interval. For example, if the analysis period is from June 1, 2005 to May 31, 2015, and the analysis interval is 1 month, the analysis period of 11 years is divided into 132 periods.

The number of periods after division is set to p. The clustering processing unit 14 performs hierarchical cluster analysis for each of the p periods in step S104. More specifically, for each of the p periods, the clustering processing unit 14 analyzes the hierarchical cluster using a statement having a date and time within the period among text data read and output in step S102 for the words extracted in step S103. Conduct. The clustering processing unit 14 obtains a degree of similarity between two words, for example, based on the distance between two words in the text data 32 (how far the two words appear apart). The clustering processing unit 14 performs hierarchical cluster analysis using a predetermined method (e.g., shortest distance method, longest distance method, group average method, decimal method, word method, etc.) based on the degree of similarity between the obtained words. .

In step S105, the clustering processing unit 14 calculates the frequency of occurrence of words for each of the p periods. In step S104, p results of the hierarchical cluster analysis are obtained, and in step S105, p occurrence frequencies of words are obtained. In step S106, the analysis result storage unit 15 stores the hierarchical cluster analysis result and the frequency of occurrence of words for each of the p periods.

Next, the screen display unit 16 displays a screen including the result of hierarchical cluster analysis stored in the analysis result storage unit 15 (step S107). 4 is a diagram showing an example of a window displayed in step S107. The window 41 shown in FIG. 4 contains the result of hierarchical cluster analysis. When the number of clusters is set for the result of hierarchical cluster analysis, words included in each cluster are determined. When displaying a screen including the result of hierarchical cluster analysis, the text mining device 10 displays a plurality of clusters in the manner shown in FIG. 4 instead of a tree diagram.

The text mining apparatus 10 has, as operation parameters, the number of clusters and the maximum number of display data in the cluster. Hereinafter, the former is m and the latter is n. These values are set to predetermined initial values in the initial state. The user may arbitrarily set these values using the instruction input unit 11. In the text mining apparatus 10, the words extracted in step S103 are classified into m clusters. Each cluster contains one or more words. In the window 41, m clusters are displayed as a cloud-shaped figure, and words included in each cluster are displayed inside each cluster. The number of words displayed inside each cluster is limited to n or less. For example, when n = 5, when a cluster contains 10 words, 5 words are displayed inside the cluster displayed on the screen.

Next, the instruction input unit 11 receives an instruction from the user (step S111). Next, the text mining apparatus 10 determines whether or not the instruction received in step S111 is an instruction for designating a target word (step S112). The control of the text mining apparatus 10 proceeds to step S121 in the case of yes, and proceeds to step S113 in the case of no.

In the latter case, the instruction received in step S111 is, for example, an instruction to move the window, an instruction to hide the window, an instruction to close the window, and the like. The screen display unit 16 displays the updated screen according to the instruction received in step S111 (step S113). After that, the control of the text mining device 10 proceeds to step S111.

When executing step S111, a screen including the result of hierarchical cluster analysis is displayed. Hereinafter, when executing step S111, it is assumed that a screen including the window 41 shown in Fig. 4 is displayed. In addition, clicking the button of the mouse 29 when the mouse cursor 43 is over a certain element in the display screen is referred to as "clicking the element", and the cluster containing the target word is referred to as "the main word cluster", The name given to the main word cluster is called "the main word cluster name".

5 is a diagram showing an operation for designating a target word. In the window 41, the user clicks a word (here "disassemble") designated as the target word (click 1st time). At this time, the context menu 42 appears in the display screen. In the context menu 42, the user clicks the item "Change of analysis result over time" (click 2nd time). By this operation, the word clicked on the first time is designated as the target word.

In the case of YES in step S112, the screen display unit 16 reads and outputs the hierarchical cluster analysis result and the frequency of occurrence of the target word from the analysis result storage unit 15 (step S121). Next, the screen display unit 16 displays a screen including a change over time in the hierarchical cluster analysis result based on the read-out data (step S122).

Fig. 6 is a diagram showing a window showing a change over time of an analysis result displayed in step S122. The window 51 shown in FIG. 6 is displayed in step S111 when "decomposition" is designated as the main word. The window 51 is superimposed on the window 41 shown in FIG. 4 as shown in FIG. 7, for example.

The window 51 includes a line graph 52 showing a change in the frequency of appearance of the attention word over time along a time axis extending in the horizontal direction. For the frequency of appearance of the attention word, for example, a ratio occupied by the number of appearances of the attention word among the sum of the number of occurrences of all words included in the attention word cluster is used. The frequency of appearance of the attention word may be changed to the number of appearances of the attention word according to an instruction from the user.

The configuration of the cluster (elements included in the cluster) obtained by hierarchical cluster analysis changes over time. Clusters are automatically named to indicate how the cluster changes over time. When the cluster contains only one word, the word is used as it is in the cluster name. When the cluster includes two words, the cluster name is the one in which the two words are connected in the order of the highest frequency of appearance. When the cluster includes three or more words, the cluster name is one in which three words with a high occurrence frequency among words included in the cluster are connected in the order of the highest occurrence frequency. Further, when the set of words constituting the cluster name is the same, even if the order of the words is different, it is treated as the same cluster name.

8A to 8D are diagrams showing examples of changes over time in the results of hierarchical cluster analysis. 8A to 8D show the results of hierarchical cluster analysis in different months. In Figs. 8A to 8D, a cloud-shaped figure represents a cluster, and an underlined character string represents a cluster name. The size of the circle indicates the frequency of occurrence of words written in the circle.

In the analysis result shown in Fig. 8A, the words extracted from the text data are clusters including "drive" and "decomposition", clusters including "exhaust" and "pressure" and "flow" and "valve", and " It is classified into clusters including “belt”, “rotation”, “check”, “motor” and “tightness”. These three clusters are named "drive/disassemble", "exhaust/pressure/flow", and "belt/rotation/check", respectively. Also about the analysis results shown in Figs. 8B to 8D, three clusters are named in the same manner.

When "decomposition" is designated as the notable word, the name of the attention word cluster is "decomposition/drive" in the analysis result shown in FIG. 8A, and "drive/belt rotation" in the analysis result shown in FIG. 8B. In the analysis result, it is "exhaust pressure, flow", and in the analysis result shown in FIG. 8D, it is "exhaust pressure, decomposition". In this way, the attention word cluster name changes over time.

The window 51 shown in FIG. 6 includes the attention word cluster name 53, the boundary line 54, and the arrow 55 in addition to the dotted line graph 52. The attention word cluster name 53 is displayed on the upper part of the broken line graph 52 along the time axis extending in the horizontal direction. The boundary line 54 is displayed in the broken line graph 52 at a position corresponding to the time when the attention word cluster name 53 changes. The attention word cluster name 53 is displayed for each period divided by the border line 54. The background of the dotted line graph 52 has a different aspect (for example, a different color or a different pattern) for each boundary line 54. Among the words constituting the attention word cluster name 53, a word changed from the previous cluster name (a word not included in the old attention word cluster name, but included in the new attention word cluster name), Is highlighted. In window 51, such words are indicated in both Gothic and italics.

An arrow 55 is displayed at a position corresponding to the time when the attention word cluster name 53 changes, above the boundary line 54. Arrow 55 is displayed in the mode according to the degree of change of the attention word cluster name 53. When all of the words constituting the attention word cluster name 53 change, a red arrow 55r is displayed. When two words constituting the attention word cluster name 53 change, a blue arrow 55b is displayed. When one word constituting the attention word cluster name 53 changes, a black arrow 55n is displayed. In addition, the display mode of the arrow 55 may be arbitrary as long as it differs according to the degree of change of the attention word cluster name 53. For example, the display size of the arrow 55 may be different depending on the degree of change of the attention word cluster name 53.

In the example shown in FIG. 6, the attention word cluster name 53 is time in the order of "drive/decomposition", "drive/belt/rotation", "exhaust/pressure/flow", and "exhaust/pressure/decomposition". It changes with progress. In the first change, two words constituting the attention word cluster name 53 change, so a blue arrow 55b is displayed on the first boundary line 54. In the second change, since all of the words constituting the attention word cluster name 53 change, a red arrow 55r is displayed on the second boundary line 54. In the third change, since one word constituting the attention word cluster name 53 changes, a black arrow 55n is displayed on the third boundary line 54.

Next, the screen display unit 16 obtains the number of arrows 55 included in the screen displayed in step S122 for each type (step S123). Next, the screen display unit 16 determines whether or not the change in the attention word cluster name 53 is large, based on the number of arrows 55 of each type (step S124). The screen display unit 16 may be determined as YES when the number of red arrows 55r exceeds 30% of the total number of arrows 55, for example, and the number of red arrows 55r and the blue arrows ( When the total number of 55b) exceeds 60% of the total number of arrows 55, it may be judged as YES. The control of the text mining device 10 proceeds to step S125 in the case of yes, and to step S111 in the case of no.

In the former case, the screen display unit 16 displays a screen including a warning message (step S125). 9 is a diagram showing a window displayed in step S125. The window 61 shown in Fig. 9 is a warning message to the effect of recommending readjusting the hierarchical cluster analysis settings (e.g., the number of clusters or the number of target words) because the configuration of the attention word cluster changes greatly in many cases. It includes. After that, control of the text mining device 10 proceeds to step S111.

As described above, in the text mining method according to the present embodiment, the steps of extracting words from text data consisting of statements having dates (steps S102 and S103), and hierarchical cluster analysis for each analysis period for the extracted words. It includes a step to perform (step S104) and a step (steps S107, S113, and S121 to S125) of displaying a screen including the analysis result by hierarchical cluster analysis. When an instruction for designating a target word is input in the first screen containing the analysis result (the screen including the window 41) (Fig. 5), the step of displaying the screen (step S122) includes the target word A second screen (a screen including the window 51) indicating changes over time of the cluster to be performed is displayed. According to the text mining method according to the present embodiment, when an instruction for designating a target word is input in the first screen including the result of hierarchical cluster analysis, it indicates the change over time of the cluster including the target word. By displaying the second screen, the user can easily recognize the change over time in the hierarchical cluster analysis result.

In addition, the second screen shows the cluster name (the main word cluster name 53) based on words included in the cluster including the target word along the time axis. In addition, this cluster name is a result of concatenating a predetermined number or less (three or less) of words included in the cluster containing the target word in the order of their appearance frequency. Accordingly, the user can easily recognize the change over time of the cluster including the target word.

Further, the second screen includes a mark having an aspect according to the degree of change of the cluster name at a position corresponding to the time when the name of the cluster including the target word changes. This mark may be an arrow 55 having a color according to the degree of change in the cluster name. By displaying the second screen including such a mark (arrow 55), the user can easily recognize the degree of change in the cluster name including the target word. In addition, among words constituting the cluster name, words that have changed from the previous cluster name ("belt", "rotation", etc. shown in Fig. 6) are highlighted in the second screen. Accordingly, the user can easily recognize how a word with a high frequency of appearance has changed in the cluster containing the target word.

In addition, the second screen includes a graph (line graph 52) showing a change over time in the frequency of appearance of the target word along the time axis. In addition to the change over time of the cluster containing the attention word, by displaying a screen including a graph representing the change over time in the frequency of appearance of the attention word, the user can Changes can be easily recognized. Further, the second screen includes a boundary line 54 at a position corresponding to the time when the name of the cluster containing the target word changes, and the background of the graph has a different aspect for each boundary line. Accordingly, the user can easily recognize when the cluster including the target word changes. In addition, when the name of the cluster containing the target word frequently changes greatly, the step of displaying the screen displays a screen including a warning message (a screen including the window 61). Thus, the user can recognize that hierarchical cluster analysis is not working well.

The text mining apparatus 10 and the text mining program 31 according to the present embodiment have the same characteristics as the text mining method described above, and exhibit the same effect. According to the text mining method, the text mining apparatus 10, and the text mining program 31 according to the present embodiment, the user can easily recognize the change over time in the hierarchical cluster analysis result.

Although the present invention has been described in detail above, the above description is illustrative in all respects and is not limiting. It is understood that many other changes or modifications may be devised without departing from the scope of the present invention.

10: text mining device
11: Instruction input unit
12: text data storage unit
13: word extraction unit
14: clustering processing unit
15: analysis result storage unit
16: screen display
20: computer
21: CPU
22: main memory
29: mouse
30: recording medium
31: text mining program
32: text data
41, 51, 61: window
42: context menu
43: mouse cursor
52: line graph
53: Note word cluster name
54: border line
55: arrow

Claims (20)

delete delete A text mining method for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
Comprising a step of displaying a screen including a result of the hierarchical cluster analysis,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
The cluster name is characterized in that the words included in the cluster are concatenated by a predetermined number or less in the order of high frequency of appearance. A text mining method for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
Comprising a step of displaying a screen including a result of the hierarchical cluster analysis,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
The second screen, characterized in that, at a position corresponding to a time when the cluster name changes, further includes a mark having a mode according to the degree of change of the cluster name. The method of claim 4,
The mark is an arrow having a color according to the degree of change of the cluster name. A text mining method for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
Comprising a step of displaying a screen including a result of the hierarchical cluster analysis,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
A text mining method, characterized in that, among words constituting the cluster name, a word changed from the previous cluster name is highlighted in the second screen. delete A text mining method for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
Comprising a step of displaying a screen including a result of the hierarchical cluster analysis,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
The second screen further includes a graph showing a change over time in the frequency of appearance of the main word along the time axis,
The second screen further includes a boundary line at a position corresponding to the time when the cluster name changes, and the background of the graph has a different aspect for each boundary line. delete delete delete A text mining program stored in a recording medium for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
The CPU executes the step of displaying a screen including the result of the hierarchical cluster analysis in the computer using a memory,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
The cluster name is a text mining program stored in a recording medium, characterized in that the words included in the cluster are concatenated by a predetermined number or less in order of high frequency of appearance. A text mining program stored in a recording medium for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
The CPU executes the step of displaying a screen including the result of the hierarchical cluster analysis in the computer using a memory,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
And the second screen further includes a mark having an aspect corresponding to a degree of change of the cluster name at a position corresponding to a time when the cluster name changes. The method of claim 13,
Wherein the mark is an arrow having a color according to a degree of change of the cluster name. A text mining program stored in a recording medium for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
The CPU executes the step of displaying a screen including the result of the hierarchical cluster analysis in the computer using a memory,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
A text mining program stored in a recording medium, characterized in that, among words constituting the cluster name, a word that has changed from a previous cluster name is highlighted in the second screen. delete A text mining program stored in a recording medium for displaying a screen including an analysis result of text data,
A step of extracting a word from text data consisting of a statement having a date,
A step of performing a hierarchical cluster analysis on the word for each analysis period,
The CPU executes the step of displaying a screen including the result of the hierarchical cluster analysis in the computer using a memory,
When an instruction for designating a target word in the first screen including the result is input, the step of displaying the screen includes displaying a second screen indicating changes over time of the cluster including the target word, and ,
The second screen displays a cluster name based on words included in the cluster along a time axis,
The second screen further includes a graph showing a change over time in the frequency of appearance of the main word along the time axis,
The second screen further includes a boundary line at a position corresponding to a time when the cluster name changes, and the background of the graph has a different aspect for each boundary line. delete A text mining device that displays a screen including an analysis result of text data,
A word extraction unit for extracting a word from text data consisting of a statement having a date,
A clustering processing unit that performs hierarchical cluster analysis on the word by analysis period,
A screen display unit for displaying a screen including a result of the hierarchical cluster analysis,
When an instruction for designating a target word is input in the first screen including the result, the screen display unit displays a second screen indicating changes over time of the cluster including the target word,
The second screen displays a cluster name based on words included in the cluster along a time axis,
Wherein the second screen further includes a mark having an aspect according to a degree of change of the cluster name at a position corresponding to a time when the cluster name changes. The method of claim 19,
Wherein the mark is an arrow having a color according to a degree of change of the cluster name.

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