CN111340385A - Scientific measuring method for measuring joy index of tourist attraction - Google Patents
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Abstract
The invention discloses a scientific measuring method for measuring a tourist attraction joy index, which relates to big data processing and comprises the steps of obtaining comment information corresponding to tourist attractions of preset types; establishing a keyword corpus lexicon by using the comment information; performing semantic analysis on the comment information by using the keyword corpus thesaurus and a preset evaluation index system to identify the emotional tendency of each comment; dividing emotion keywords of the identified emotional tendency of the comment to obtain two relative emotional words and a neutral emotional word; assigning the emotion words corresponding to each comment according to a preset rule; and accumulating the emotional words corresponding to the comments after the assignment so as to obtain the net favorable comment index of the tourist attraction corresponding to the comments. The invention can provide a relatively accurate emotion evaluation index for audiences by utilizing the net good evaluation index, has better referential performance and is beneficial to tourists to select tourist attractions.
Description
Technical Field
The invention relates to a big data processing technology, in particular to a scientific measuring method for measuring a pleasure index of a tourist attraction.
Background
Based on the quality grade division and evaluation standards of Chinese tourist attractions, the method mainly evaluates each index of the scenic spots in an expert angle and finally evaluates the corresponding scenic spots, and is lack of the current situation of dynamically, intuitively and quickly judging the quality index of the tourist attractions.
With the rapid development of the internet, more tourism consumption is shifted from offline to online, more subjective comment information on a third-party professional tourism website is increased, and even explosive growth is realized. More and more people or institutions are becoming accustomed to searching review information over the web to help select a play scene to make a decision. However, the huge amount of information makes them have to manually browse, check and judge the information one by one among the huge amount of comments after searching so as to make comprehensive judgment. However, in reality, it is difficult to re-process and classify and review such huge amounts of review information, and it is difficult to obtain review information from viewpoints.
Disclosure of Invention
The invention provides a scientific measuring method for measuring a pleasure index of a tourist attraction, aiming at the problem that the existing massive comment information is not beneficial to the selection of tourists.
The technical scheme provided by the invention for the technical problem is as follows:
a scientific metrology method of measuring a enjoyment index of a tourist attraction, the method comprising:
obtaining comment information corresponding to a preset type of tourist attraction;
establishing a keyword corpus lexicon by using the comment information;
performing semantic analysis on the comment information by using the keyword corpus thesaurus and a preset evaluation index system to identify the emotional tendency of each comment;
performing emotion keyword division on the emotion tendencies of the identified comments to obtain two relative emotion words and a neutral emotion word, wherein the two relative emotion words comprise an emotion word characterized as joy and an emotion word characterized as unhappy, and the neutral emotion word comprises an emotion word characterized as no emotion tendencies;
assigning a value to the emotion word corresponding to each comment according to a preset rule, wherein the emotion word represented as joy is assigned with a positive value; assigning negative values to the emotional words characterized by being not happy, and assigning zero to the emotional words characterized by having no emotional tendency;
and accumulating the emotional words corresponding to the comments after the assignment so as to obtain the net favorable comment index of the tourist attraction corresponding to the comments.
Preferably, after the emotion words corresponding to the assigned comments are accumulated to obtain the net favorable rating index of the tourist attraction corresponding to the comments, the method further includes:
acquiring the ratio of the net favorable evaluation index of the current tourist attraction to the total net favorable evaluation index of all the tourist attractions;
multiplying the ratio by a preset constant to obtain a joy index.
Preferably, when the number of the comments is in the hundred thousand unit levels, the preset constant is 10000.
Preferably, the emotional words characterized as joy are assigned a value of 1; an emotional word characterized as being not happy is assigned a value of-1.
Preferably, before the obtaining of the comment information corresponding to the preset kind of tourist attraction, the method includes:
acquiring a tourist attraction list and attraction information corresponding to the tourist attractions in the list;
and the scenic spot information is utilized to classify the scenic spots according to preset rules to obtain the preset type of scenic spots.
Preferably, the preset kind of tourist attraction comprises one or more of the following: theme parks, natural landscapes, human landscapes and museums.
Preferably, the establishing a keyword corpus thesaurus by using the comment information includes:
performing word frequency analysis on the comment information;
and taking words and sentences reaching the preset word frequency as keywords to establish a keyword corpus lexicon.
Preferably, after the comment information is subjected to semantic analysis by using the keyword corpus thesaurus and a preset evaluation index system to identify the emotional tendency of each comment, the method further comprises:
and feeding back corresponding keywords to the keyword corpus lexicon for machine learning so as to expand the word and sentence volume of the keyword corpus lexicon.
Preferably, the preset evaluation index system at least comprises a tourist attraction index system.
Preferably, the obtaining of the comment information corresponding to the preset type of tourist attraction includes:
and obtaining comment information corresponding to the preset type tourist attraction within a preset time interval.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
establishing a keyword corpus thesaurus by using the acquired comment information, and performing semantic analysis on the comment information by using the keyword corpus thesaurus and a preset evaluation index system to identify the emotional tendency of each comment. Secondly, dividing emotion keywords of the identified emotional tendency of the comment to obtain two opposite emotional words and a neutral emotional word, wherein the two opposite emotional words comprise emotional words characterized as happy and emotional words characterized as not happy, and the neutral emotional word comprises emotional words characterized as not having emotional tendency; assigning a value to the emotion word corresponding to each comment according to a preset rule, wherein the emotion word represented as joy is assigned with a positive value; assigning negative values to the emotional words characterized by being not happy, and assigning zero to the emotional words characterized by having no emotional tendency; and accumulating the emotional words corresponding to the comments after the assignment so as to obtain the net favorable comment index of the tourist attraction corresponding to the comments. The net good comment index can be used for providing a relatively accurate emotion comment index for audiences, has good referential performance and is beneficial to tourists to select tourist attractions. Compared with the situation that each evaluation page has a good evaluation or an error evaluation in the traditional method, the method can realize more accurate evaluation mining.
In addition, the net good scores of each tourist attraction after the semantic analysis data is arranged are used as a calculation starting point, the previous multi-dimensional data calculation is converted into single-dimensional calculation and comparison, and meanwhile, the indexes are converted through the percentage score principle generally recognized by the result masses, so that the data comparison performance and the result directivity of the obtained joy index are stronger.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a scientific method for measuring a enjoyment index of a tourist attraction according to an embodiment of the present invention;
fig. 2 is a flowchart of a scientific measuring method for measuring a pleasure index of a tourist attraction according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides a comprehensive reference index for tourists and tourism service related providers based on the comprehensive data of tourist sites, so as to be beneficial to providing better tourism selection and service quality.
Referring to fig. 1, a flowchart of a scientific measuring method for measuring a joy index of a tourist attraction provided by the present invention is shown in an embodiment. The scientific measuring method for measuring the joy index of the tourist attraction comprises the following steps:
s101: the method comprises the following steps of obtaining comment information corresponding to a preset type tourist attraction, wherein the preset type tourist attraction can be obtained through the following modes:
acquiring a tourist attraction list and attraction information corresponding to the tourist attractions in the list;
and the scenic spot information is utilized to classify the scenic spots according to preset rules to obtain the preset type of scenic spots. Here, the preset rule may be determined according to the core attraction of the tourist attraction, that is, the content of the core attraction of the tourist attraction is used as the classification rule to obtain the tourist attraction of the preset kind.
In this step, the comment information corresponding to the preset type of tourist attraction may include a user comment provided by an official website platform corresponding to the tourist attraction and/or a third party online platform providing online services for the tourist attraction and a comment structure, and the user comment may be obtained by a crawler tool or a manual entry manner.
The preset kinds of tourist attractions comprise one or more of the following: theme parks, natural landscapes, human landscapes and museums.
It is understood that the comment information is a general term for information containing one comment or more than one comment.
S102: and establishing a keyword corpus lexicon by using the comment information.
Specifically, this step can be implemented as follows:
firstly: and performing word frequency analysis on the comment information, more specifically, dividing each comment content in the comment information according to a preset word segmentation rule to obtain multiple groups of segmented words, and then performing word frequency analysis on the multiple groups of segmented words obtained by segmenting all comments in the comment information to count the occurrence frequency of each phrase and/or a sentence including the phrase.
And secondly, taking words and sentences reaching the preset word frequency as keywords to establish a keyword corpus lexicon.
S103: and performing semantic analysis on the comment information by using the keyword corpus thesaurus and a preset evaluation index system to identify the emotional tendency of each comment.
The preset evaluation index system at least comprises a tourist attraction index system, wherein the tourist attraction index system can comprise 11 types of first-level indexes and 56 sub-types of second-level indexes, and the specific evaluation index system can be as follows:
table 1: tourist attraction evaluation index system
According to each index in the table 1, the attention points of tourists to tourist attractions can be reflected, comments of the attention points of the same type can be found in comment information by using the attention points, and keywords in the comments are found in combination with the keywords in the keyword corpus thesaurus so as to be beneficial to obtaining corresponding emotional tendencies.
It is understood that semantic analysis (semantic analysis) refers to learning and understanding semantic content represented by a piece of text by using various methods, and any understanding of a language can be classified into the category of semantic analysis. By establishing an effective model and an effective system, automatic semantic analysis of each language unit (including vocabulary, sentences, chapters and the like) is realized, so that the real semantics of the whole text expression is understood. The method is characterized in that the text sentiment is analyzed through semantic analysis, and the text with subjective information is taken as a research object, so that the sentiment, the viewpoint and the influence expressed in the text are identified, classified, extracted and labeled.
In this step, after performing semantic analysis on the comment information by using the keyword corpus thesaurus and the preset evaluation index system to identify the emotional tendency of each comment, the method may further include: and feeding back corresponding keywords to the keyword corpus thesaurus for machine learning so as to expand the word and sentence amount of the keyword corpus thesaurus, so that the keyword corpus thesaurus can dynamically update the word and sentence amount, the adaptability to the comment words of the tourist is further improved, and the obtained semantics are closer to the emotional expression of the tourist.
In this step, it can be identified whether each comment includes an emotional meaning through semantic analysis, that is, it is identified that the comment includes an emotional tendency, and the process can proceed to step S104.
S104: and carrying out emotion keyword division on the emotion tendencies of the identified comments to obtain two relative emotion words and a neutral emotion word, wherein the two relative emotion words comprise emotion words characterized as happy and emotion words characterized as unhappy, and the neutral emotion word comprises emotion words characterized as no emotion tendencies.
In this step, the two relative emotional words mean that the emotions expressed by the two emotional words are at least opposite, if one of the two emotional words is characterized as happy emotional word such as "happy", and the other is characterized as unhappy emotional word such as "unhappy", and the emotional words characterized as non-emotional tendency may be "feeble", "general", and the like.
It is to be understood that the two opposing emotive words listed above can also be replaced with other emotive words such as "active" versus "passive", "happy" versus "unhappy".
It is understood that a conclusion of good or bad comment can be made on the current comment as a whole according to two relative emotional words.
S105: assigning a value to the emotion word corresponding to each comment according to a preset rule, wherein the emotion word represented as joy is assigned with a positive value; the emotional words characterized as being not happy are assigned negative values, and the emotional words characterized as being not emotional tendency are assigned zero values.
S106: and accumulating the emotional words corresponding to the comments after the assignment so as to obtain the net favorable comment index of the tourist attraction corresponding to the comments.
In the embodiment, a keyword corpus thesaurus is established by using the obtained comment information, and semantic analysis is performed on the comment information by using the keyword corpus thesaurus and a preset evaluation index system so as to identify the emotional tendency of each comment. Secondly, dividing emotion keywords of the identified emotional tendency of the comment to obtain two opposite emotional words and a neutral emotional word, wherein the two opposite emotional words comprise emotional words characterized as happy and emotional words characterized as not happy, and the neutral emotional word comprises emotional words characterized as not having emotional tendency; assigning a value to the emotion word corresponding to each comment according to a preset rule, wherein the emotion word represented as joy is assigned with a positive value; assigning negative values to the emotional words characterized by being not happy, and assigning zero to the emotional words characterized by having no emotional tendency; and accumulating the emotional words corresponding to the comments after the assignment so as to obtain the net favorable comment index of the tourist attraction corresponding to the comments. The net good comment index can be used for providing a relatively accurate emotion comment index for audiences, has good referential performance and is beneficial to tourists to select tourist attractions.
Compared with the situation that each evaluation page has a good evaluation or an error evaluation in the traditional method, the method can realize more accurate evaluation mining.
It should be understood that, in practical application, if a piece of comment information in a certain tourist attraction contains both emotional words characterized as happy and emotional words characterized as not happy, then:
the evaluation method comprises the steps that each sentiment word in the comment can be assigned to obtain the evaluation which is integrally reflected by the comment, for example, good evaluation or poor evaluation can be assigned to the sentiment word which is characterized as happy, and poor evaluation can be assigned to the sentiment word which is characterized as unhappy, so that when the net good evaluation index is obtained, the evaluation which is integrally reflected by the comment and the evaluation which is integrally reflected by other comments can be assigned and accumulated; or
And acquiring emotional words characterized as joy and emotional words characterized as not joy in all the comments, and summing the value-added accumulation (positive value) determined by the quantity of the emotional words characterized as joy and the value-added accumulation (negative value) determined by the quantity of the emotional words characterized as not joy to acquire the net favorable comment index.
In this embodiment, in order to make the data directional according to time, the aforementioned obtaining of the comment information corresponding to the preset type of tourist attraction may specifically include obtaining comment information corresponding to the preset type of tourist attraction within a preset time interval.
Referring to fig. 2, a flow chart of a scientific measuring method for measuring a pleasure index of a tourist attraction provided by the present invention in another embodiment is shown. Compared with the previous embodiment, the present embodiment can obtain the joy index of the tourist attraction on the basis of the previous embodiment, so as to further quantify the experience of the tourist and provide better reference selection for the tourist.
The scientific measuring method for measuring the joy index of the tourist attraction of the embodiment can comprise the following steps:
s201: and obtaining comment information corresponding to the preset tourist attraction.
S202: and establishing a keyword corpus lexicon by using the comment information.
S203: and performing semantic analysis on the comment information by using the keyword corpus thesaurus and a preset evaluation index system to identify the emotional tendency of each comment.
S204: and carrying out emotion keyword division on the emotion tendencies of the identified comments to obtain two relative emotion words and a neutral emotion word, wherein the two relative emotion words comprise emotion words characterized as happy and emotion words characterized as unhappy, and the neutral emotion word comprises emotion words characterized as no emotion tendencies.
S205: assigning a value to the emotion word corresponding to each comment according to a preset rule, wherein the emotion word represented as joy is assigned with a positive value; the emotional words characterized as being not happy are assigned negative values, and the emotional words characterized as being not emotional tendency are assigned zero values.
S206: and accumulating the emotional words corresponding to the comments after the assignment so as to obtain the net favorable comment index of the tourist attraction corresponding to the comments.
S207: a ratio of the net goodness index for the current tourist attraction to the total net goodness index for all tourist attractions currently acquired is obtained.
S208: multiplying the ratio by a preset constant to obtain a joy index. Here, the joy index may mainly comprise two layers of meaning, i.e., the degree of popularity of a scenic spot, and the degree of satisfaction of the scenic spot.
In this embodiment, the emotional words characterized as joy may be assigned a value of 1; an emotional word characterized as being not happy may be assigned a value of-1, then:
if the review information of n tourist attractions in the country is selected, and the month and the year are used as the time period, the keyword corpus thesaurus and the preset evaluation index system are also used for performing semantic analysis on the review information, so that the joy index (THI) can meet the following formula:
NPi＝Pi-Bi… … … … … … formula 1
In the formula:
goodness of Pi scenic spot i
Bad evaluation of Bi scenic spot i
NPi net goodness of scenic spot i
Total net goodness of GNP n scenic spots
i scenic spot (i ═ 1, 2, 3. n)
K constant, the ratio of the total net merit number to 10000, i.e. if the number of scenic spots changes or is adjusted, the constant will change accordingly. In the formula, the preset constant is 10000, the value 10000 is calculated based on the percentage system of public consensus on the index, the total comment data volume of each tourist attraction obtained by monthly and annual statistics is hundreds of thousands of grades, and the value 10000 is used for limiting the index within 100 as much as possible.
Joy index (THI) of the THIi scenic spot i
On the basis of the above embodiment, the embodiment takes the net good scores of each tourist attraction after the data is sorted by semantic analysis as a calculation starting point, converts the previous calculation of multi-dimensional data into the calculation and comparison of single dimension, and converts the index through the percentile scoring principle generally recognized by the result masses, so that the data comparison and the result directivity of the obtained joy index are stronger.
In addition, the scientific metering method for measuring the pleasure index of the scenic spot based on the big data can optimize the traditional method for evaluating the quality of the scenic spot by only expert evaluation and sampling survey, and comprehensively, objectively and systematically evaluate the scenic spot by adopting a full-coverage and big data research method; in addition, the evaluation scenic spots from top to bottom in the management level are changed, and the evaluation scenic spots are viewed from the perspective of the experience of visitors from bottom to top; and moreover, by applying a big data algorithm, the non-quantifiable emotion, feeling and experience are changed into visual, visual and measurable data.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A scientific method for measuring the joy index of tourist attractions, comprising:
obtaining comment information corresponding to a preset type of tourist attraction;
establishing a keyword corpus lexicon by using the comment information;
performing semantic analysis on the comment information by using the keyword corpus thesaurus and a preset evaluation index system to identify the emotional tendency of each comment;
performing emotion keyword division on the emotion tendencies of the identified comments to obtain two relative emotion words and a neutral emotion word, wherein the two relative emotion words comprise an emotion word characterized as joy and an emotion word characterized as unhappy, and the neutral emotion word comprises an emotion word characterized as no emotion tendencies;
assigning a value to the emotion word corresponding to each comment according to a preset rule, wherein the emotion word represented as joy is assigned with a positive value; assigning negative values to the emotional words characterized by being not happy, and assigning zero to the emotional words characterized by having no emotional tendency;
and accumulating the emotional words corresponding to the comments after the assignment so as to obtain the net favorable comment index of the tourist attraction corresponding to the comments.
2. The scientific metering method for measuring the enjoyment index of tourist attractions of claim, wherein after the step of accumulating the emotional words corresponding to the assigned reviews to obtain the net good appreciation index of tourist attractions corresponding to the reviews, the method further comprises the following steps:
acquiring the ratio of the net favorable evaluation index of the current tourist attraction to the total net favorable evaluation index of all the tourist attractions;
multiplying the ratio by a preset constant to obtain a joy index.
3. The scientific metering method for measuring the joy index of tourist attraction of claim 2, wherein the preset constant is 10000 when the number of the comments is in the order of one hundred thousand units.
4. The scientific method of measuring the joy index of tourist attractions of claim 3 wherein the emotion word characterized by joy is assigned a value of 1; an emotional word characterized as being not happy is assigned a value of-1.
5. The scientific metering method for measuring the joy index of tourist attractions according to any one of claims 1 to 4, wherein before the obtaining of the comment information corresponding to tourist attractions of the preset variety, the method comprises:
acquiring a tourist attraction list and attraction information corresponding to the tourist attractions in the list;
and the scenic spot information is utilized to classify the scenic spots according to preset rules to obtain the preset type of scenic spots.
6. The scientific metering method for measuring the joy index of tourist attractions according to claim 5 wherein the tourist attractions of the preset category comprise one or more of the following: theme parks, natural landscapes, human landscapes and museums.
7. The scientific metering method for measuring the joy index of tourist attractions as claimed in claim 1, wherein the establishing the keyword corpus thesaurus by using the comment information comprises:
performing word frequency analysis on the comment information;
and taking words and sentences reaching the preset word frequency as keywords to establish a keyword corpus lexicon.
8. The scientific metering method for measuring the joy index of tourist attractions as claimed in claim 1, wherein after the comment information is semantically analyzed by using the keyword corpus and the preset evaluation index system to identify the emotional tendency of each comment, the method further comprises:
and feeding back corresponding keywords to the keyword corpus lexicon for machine learning so as to expand the word and sentence volume of the keyword corpus lexicon.
9. The scientific metrology method of tourist attraction joy index of claim 1 wherein the preset evaluation index system comprises at least a tourist attraction index system.
10. The scientific metering method for measuring the joy index of tourist attractions as claimed in claim 1, wherein the obtaining of comment information corresponding to tourist attractions of a preset category comprises:
and obtaining comment information corresponding to the preset type tourist attraction within a preset time interval.
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| CN109034893A (en) * | 2018-07-20 | 2018-12-18 | 成都中科大旗软件有限公司 | A kind of tourist net comment sentiment analysis and QoS evaluating method |
| CN110059922A (en) * | 2019-03-11 | 2019-07-26 | 北京比速信息科技有限公司 | Satisfaction evaluation method on the line of data is commented on based on internet tourist |
| CN110705286A (en) * | 2019-09-24 | 2020-01-17 | 青木数字技术股份有限公司 | Comment information-based data processing method and device |
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| CN112256852A (en) * | 2020-10-28 | 2021-01-22 | 北京软通智慧城市科技有限公司 | Scenic spot comment data processing method and device, electronic equipment and storage medium |
| CN113298367A (en) * | 2021-05-12 | 2021-08-24 | 北京信息科技大学 | Theme park perception value evaluation method |
| CN113298367B (en) * | 2021-05-12 | 2023-12-12 | 北京信息科技大学 | Theme park perception value evaluation method |
| CN113657766A (en) * | 2021-08-18 | 2021-11-16 | 深圳华侨城创新研究院有限公司 | Tourist attraction joy index metering method based on tourist multi-metadata |
| CN114429384A (en) * | 2021-12-30 | 2022-05-03 | 杭州盟码科技有限公司 | Intelligent product recommendation method and system based on e-commerce platform |
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