Emotional state evaluation method based on text data collection and analysis

Abstract

The invention discloses an emotional state assessment method based on text data collection and analysis, and the method comprises the following steps: S1, obtaining the original data of a to-be-assessed emotional state and related feature information. The method has the beneficial effects that the original data of the emotional state and the related feature information are obtained in various modes; therefore, various reference bases can be provided for subsequent emotional state evaluation, useless data information is eliminated before reference values are provided, the evaluation result is prevented from being affected, an emotional state model is constructed by using a naive Bayesian algorithm and collected and analyzed text data information, and the evaluation accuracy is improved. According to the method, the difference enables the obtained data to be more comprehensive and specific, more importantly, the data can be visually displayed through a model, matching is carried out through more diversified collection and analysis tools and modes, and then a more accurate evaluation conclusion is obtained by inputting text data information of related emotional states.

Description

Technical Field

[0001] The present invention relates to the technical field of emotional state assessment, and in particular to an emotional state assessment method based on text data collection and analysis. Background Art

[0002] Emotional state assessment refers to the process of measuring and evaluating an individual's emotional state through a series of methods and tools. Emotional state assessment not only focuses on the individual's emotional experience, but also involves the understanding and classification of their emotional response patterns.

[0003] Traditional emotional state assessment methods can usually only give the two extremes of the overall emotional state, with the emotional state being on both the positive and negative sides, which makes the assessment conclusions given relatively general and unable to provide further detailed specific emotional states. In addition, the data collected and obtained by traditional emotional state assessments are relatively limited and single, and thus cannot provide more accurate reference value, which affects the assessment conclusions of the emotional state. Summary of the invention

[0004] The purpose of the present invention is to provide an emotional state assessment method based on text data collection and analysis to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for evaluating emotional state based on text data collection and analysis, comprising the following contents:

[0006] S1: Obtain the original data of the emotional state to be evaluated and related feature information;

[0007] S2: Based on the acquired raw data and related feature information, the Naive Bayes algorithm is used to construct an emotional state assessment model;

[0008] S3: Processing and analysis based on the collected raw data;

[0009] S4: After completing the processing and analysis of the raw data, make a preliminary analysis and evaluation of the emotional state;

[0010] S5: Based on the preliminary analysis and evaluation of the emotional state and the raw data processed and analyzed, the emotional state assessment is completed using a variety of tools and methods;

[0011] S6: Based on the information collected and analyzed from the text data, a new emotional state evaluation model is established, and the information of the target emotional state is input to obtain the evaluation results.

[0012] Preferably, the step S1 includes the following:

[0013] S11: using crawler tools to automatically obtain web page text information of the original evaluation emotional state of the web page;

[0014] S12: using the API to obtain text data information of the original assessed emotional state of a specific platform;

[0015] S13: Search and download text data information from open data sets on the Internet;

[0016] S14: Query the text data information in the database by writing SQ;

[0017] S15: Acquire text data information input by the user in an interactive manner;

[0018] S16: Manually collect, organize and save text data information from different sources;

[0019] S17: After completing the collection of text data, pre-sort and analyze the data information collected by different methods, and obtain the required useful original data information through analysis;

[0020] S18: After the analysis is completed, relevant feature information is collected in the same manner.

[0021] Preferably, step S2 includes the following:

[0022] S21: Integrate the collected original data information of emotional state and related feature information;

[0023] S22: Use the naive Bayes algorithm to calculate the classification probability of the input data through the known feature conditional probability;

[0024] S23: and selecting the category with the maximum probability as the classification result;

[0025] S24: Based on the acquired original data information of the emotional state, relevant feature information and the Naive Bayes algorithm, an emotional state evaluation model is constructed, and the trend of the emotional state is intuitively displayed through the emotional state evaluation model.

[0026] Preferably, step S3 includes the following:

[0027] S31: based on obtaining the collected raw data and related feature information, integrating and processing them on the basis of the emotional state model;

[0028] S32: After completing the processing of the data information, a logistic regression algorithm is used to analyze and obtain useful information and conclusions;

[0029] S33: Relevant useful information is retained and useless information is removed in a timely manner.

[0030] Preferably, the step S4 includes the following:

[0031] S41: After the above processing and analysis of the original data, the processed and analyzed data information is converted into text data;

[0032] S42: inputting relevant information based on the text data and the established emotional state model;

[0033] S43: And extract relevant and effective features from the data in the model through deep learning technology;

[0034] S44: Then, the preliminary emotional state is displayed through the extracted effective features to obtain relevant analysis and evaluation conclusions.

[0035] Preferably, step S5 includes the following:

[0036] S51: After completing and obtaining a preliminary analysis and evaluation conclusion, compare and analyze the original processed and analyzed data according to the evaluation conclusion;

[0037] S52: Obtain significant text data features through comparative analysis.

[0038] Preferably, step S5 includes the following:

[0039] S53: Use self-report scales, operational tests, projective tests, situational simulation tests, physiological index measurements, and neuro-emotional techniques to accurately assess and process emotional states;

[0040] S54: And based on the above diverse methods, multiple assessment methods are used in conjunction with multiple tools such as questionnaires, heart rate and blood pressure and skin conductance monitoring instruments, as well as functional magnetic resonance imaging and positron emission tomography.

[0041] Preferably, step S6 includes the following:

[0042] S61: converting the data information obtained by using various tools and various evaluation methods into text data, and performing processing and analysis after completing the collection work;

[0043] S62: Then, the data information after the above text data collection, processing and analysis is used to establish a new emotional state assessment model using an algorithm.

[0044] Preferably, step S6 includes the following:

[0045] S63: using the re-established emotional state assessment model to input relevant data information;

[0046] S64: Then, an accurate evaluation conclusion is obtained based on the relevant characteristic data information.

[0047] Compared with the prior art, the beneficial effects of the present invention are: by adopting a variety of methods to obtain the original data of emotional state and related characteristic information, it can provide a variety of reference bases for subsequent emotional state evaluation, and at the same time, before providing reference value, useless data information is eliminated to prevent affecting the evaluation results, and the emotional state model is constructed by using the naive Bayes algorithm and the collected and analyzed text data information. The difference makes the obtained data more comprehensive and specific, and more importantly, it can be intuitively displayed through the model, and matched with more diverse collection and analysis tools and methods, and then by inputting the relevant emotional state text data information, a more accurate evaluation conclusion can be obtained, thereby eliminating the difference in conclusions due to text data collection and analysis data, and making the emotional state evaluation conclusion more specific, and no general and vague concepts will appear. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1 The present invention provides a technical solution: a method for evaluating emotional state based on text data collection and analysis, comprising the following contents:

[0051] S1: Obtain the original data of the emotional state to be evaluated and related feature information;

[0052] S2: Based on the acquired raw data and related feature information, the Naive Bayes algorithm is used to construct an emotional state assessment model;

[0053] S3: Processing and analysis based on the collected raw data;

[0054] S4: After completing the processing and analysis of the raw data, make a preliminary analysis and evaluation of the emotional state;

[0055] S5: Based on the preliminary analysis and evaluation of the emotional state and the original data processed and analyzed, the emotional state assessment is completed using a variety of tools and methods;

[0056] S6: Based on the information collected and analyzed from the text data, a new emotional state evaluation model is established, and the information of the target emotional state is input to obtain the evaluation results.

[0057] Wherein, the step S1 includes the following:

[0058] S11: using crawler tools to automatically obtain web page text information of the original evaluation emotional state of the web page;

[0059] S12: using the API to obtain text data information of the original assessed emotional state of a specific platform;

[0060] S13: Search and download text data information from open data sets on the Internet;

[0061] S14: Query the text data information in the database by writing SQ;

[0062] S15: Acquire text data information input by the user in an interactive manner;

[0063] S16: Manually collect, organize and save text data information from different sources;

[0064] S17: After completing the collection of text data, pre-sort and analyze the data information collected by different methods, and obtain the required useful original data information through analysis;

[0065] S18: After the analysis is completed, relevant feature information is collected in the same manner;

[0066] Wherein, the step S2 includes the following:

[0067] S21: Integrate the collected original data information of emotional state and related feature information;

[0068] S22: Use the naive Bayes algorithm to calculate the classification probability of the input data through the known feature conditional probability;

[0069] S23: and selecting the category with the maximum probability as the classification result;

[0070] S24: constructing an emotional state evaluation model based on the acquired emotional state original data information, relevant feature information and the naive Bayes algorithm, and intuitively displaying the emotional state trend through the emotional state evaluation model;

[0071] Wherein, the step S3 includes the following:

[0072] S31: based on obtaining the collected raw data and related feature information, integrating and processing them on the basis of the emotional state model;

[0073] S32: After completing the processing of the data information, a logistic regression algorithm is used to analyze and obtain useful information and conclusions;

[0074] S33: Retain relevant useful information and remove useless information in a timely manner;

[0075] Wherein, the step S4 includes the following:

[0076] S41: After the above processing and analysis of the original data, the processed and analyzed data information is converted into text data;

[0077] S42: inputting relevant information based on the text data and the established emotional state model;

[0078] S43: And extract relevant and effective features from the data in the model through deep learning technology;

[0079] S44: Then, the preliminary emotional state is displayed through the extracted effective features to obtain relevant analysis and evaluation conclusions;

[0080] Wherein, the step S5 includes the following:

[0081] S51: After completing and obtaining a preliminary analysis and evaluation conclusion, compare and analyze the original processed and analyzed data according to the evaluation conclusion;

[0082] S52: Obtain significant text data features through comparative analysis;

[0083] Wherein, the step S5 includes the following:

[0084] S53: Use self-report scales, operational tests, projective tests, situational simulation tests, physiological index measurements, and neuro-emotional techniques to accurately assess and process emotional states;

[0085] S54: And based on the above multiple methods, multiple assessment methods are used in combination with multiple tools such as questionnaires, heart rate, blood pressure and skin conductance monitoring instruments, functional magnetic resonance imaging and positron emission tomography;

[0086] Wherein, the step S6 includes the following:

[0087] S61: converting the data information obtained by using various tools and various evaluation methods into text data, and performing processing and analysis after completing the collection work;

[0088] S62: Then, the data information after the above text data collection, processing and analysis is used to establish a new emotional state assessment model using an algorithm;

[0089] Wherein, the step S6 includes the following:

[0090] S63: using the re-established emotional state assessment model to input relevant data information;

[0091] S64: Then, an accurate evaluation conclusion is obtained based on the relevant characteristic data information.

[0092] Specifically, when using the present invention, a crawler tool is used to automatically obtain web page text information of the original evaluation emotional state of the web page, an API is used to obtain text data information of the original evaluation emotional state of a specific platform, text data information is searched and downloaded from an open data set on the Internet, text data information in a database is queried by writing an SQ, text data information input by a user is obtained in an interactive manner, text data information is manually collected, sorted and saved from different sources, and after the collection of text data is completed, the data information collected in different ways is pre-sorted and analyzed, and the required useful original data information is obtained by analysis, and after the analysis is completed, the relevant feature information is collected in the same manner, S21: The collected original data information of emotional state and related feature information are integrated and processed, and the classification probability of the input data is calculated by using the naive Bayes algorithm through the known feature conditional probability, and the category with the largest probability is selected as the classification result, and the emotional state evaluation model is constructed based on the acquired original data information of emotional state, related feature information and the naive Bayes algorithm, and the trend of emotional state is intuitively displayed through the emotional state evaluation model, S31: based on the acquisition of the collected original data and related feature information, and integrated processing is performed on the basis of the emotional state model, after completing the processing of the data information, the logistic regression algorithm is used to analyze and obtain useful information and conclusions, and the relevant Useful information is obtained, and useless information is promptly eliminated. After the above processing and analysis of the original data, the processed and analyzed data information is converted into text data. Relevant information is input based on the text data and the established emotional state model, and relevant effective features are extracted from the data in the model through deep learning technology. Then, the preliminary emotional state is displayed through the extracted effective features to complete the relevant analysis and evaluation conclusions. After completing and obtaining the preliminary analysis and evaluation conclusions, the original processed and analyzed data is compared and analyzed according to the evaluation conclusions. Through the comparison and analysis, significant text data features are obtained. S53: Using self-report scales, operational tests, projective tests, situational simulation tests, and biological The emotional state is accurately evaluated and processed by psychological index measurement method and neurological influence technology, and at the same time, various evaluation methods are coordinated with various tools such as questionnaires, heart rate and blood pressure and skin conductance monitoring instruments, functional magnetic resonance imaging and positron emission tomography. The data information obtained by various tools and various evaluation methods are converted into text data, and processed and analyzed after the collection work is completed. Then, the data information after the above text data collection and processing and analysis is used to establish a new emotional state evaluation model using an algorithm, and the re-established emotional state evaluation model is used to input relevant data information, and then accurate evaluation conclusions are obtained based on this relevant feature data information.

[0093] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating emotional state based on text data collection and analysis, characterized in that: Includes the following: S1: Obtain the original data of the emotional state to be evaluated and related feature information; S2: Based on the acquired raw data and related feature information, the Naive Bayes algorithm is used to construct an emotional state assessment model; S3: Processing and analysis based on the collected raw data; S4: After completing the processing and analysis of the raw data, make a preliminary analysis and evaluation of the emotional state; S5: Based on the preliminary analysis and evaluation of the emotional state and the original data processed and analyzed, the emotional state assessment is completed using a variety of tools and methods; S6: Based on the information collected and analyzed from the text data, a new emotional state evaluation model is established, and the information of the target emotional state is input to obtain the evaluation results.

2. The method for evaluating emotional state based on text data collection and analysis according to claim 1, characterized in that: The step S1 includes the following: S11: using crawler tools to automatically obtain web page text information of the original evaluation emotional state of the web page; S12: using the API to obtain text data information of the original assessed emotional state of a specific platform; S13: Search and download text data information from open data sets on the Internet; S14: Query the text data information in the database by writing SQ; S15: Acquire text data information input by the user in an interactive manner; S16: Manually collect, organize and save text data information from different sources; S17: After completing the collection of text data, pre-sort and analyze the data information collected by different methods, and obtain the required useful original data information through analysis; S18: After the analysis is completed, relevant feature information is collected in the same manner.

3. The method for evaluating emotional state based on text data collection and analysis according to claim 2, characterized in that: The step S2 includes the following: S21: Integrate the collected original data information of emotional state and related feature information; S22: Use the naive Bayes algorithm to calculate the classification probability of the input data through the known feature conditional probability; S23: and selecting the category with the maximum probability as the classification result; S24: Based on the acquired original data information of the emotional state, relevant feature information and the Naive Bayes algorithm, an emotional state evaluation model is constructed, and the trend of the emotional state is intuitively displayed through the emotional state evaluation model.

4. The method for evaluating emotional state based on text data collection and analysis according to claim 3, characterized in that: The step S3 includes the following: S31: based on obtaining the collected raw data and related feature information, integrating and processing them on the basis of the emotional state model; S32: After completing the processing of the data information, a logistic regression algorithm is used to analyze and obtain useful information and conclusions; S33: Relevant useful information is retained and useless information is removed in a timely manner.

5. The method for evaluating emotional state based on text data collection and analysis according to claim 4, characterized in that: The step S4 includes the following: S41: After the above processing and analysis of the original data, the processed and analyzed data information is converted into text data; S42: inputting relevant information based on the text data and the established emotional state model; S43: And extract relevant and effective features from the data in the model through deep learning technology; S44: Then, the preliminary emotional state is displayed through the extracted effective features to obtain relevant analysis and evaluation conclusions.

6. The method for evaluating emotional state based on text data collection and analysis according to claim 5, characterized in that: The step S5 includes the following: S51: After completing and obtaining a preliminary analysis and evaluation conclusion, compare and analyze the original processed and analyzed data according to the evaluation conclusion; S52: Obtain significant text data features through comparative analysis.

7. The method for evaluating emotional state based on text data collection and analysis according to claim 6, characterized in that: The step S5 includes the following: S53: Use self-report scales, operational tests, projective tests, situational simulation tests, physiological index measurements, and neuro-emotional techniques to accurately assess and process emotional states; S54: And based on the above diverse methods, multiple assessment methods are used in conjunction with multiple tools such as questionnaires, heart rate and blood pressure and skin conductance monitoring instruments, as well as functional magnetic resonance imaging and positron emission tomography.

8. The method for evaluating emotional state based on text data collection and analysis according to claim 7, characterized in that: The step S6 includes the following: S61: converting the data information obtained by using various tools and various evaluation methods into text data, and performing processing and analysis after completing the collection work; S62: Then, the data information after the above text data collection, processing and analysis is used to establish a new emotional state assessment model using an algorithm.

9. The method for evaluating emotional state based on text data collection and analysis according to claim 8, characterized in that: The step S6 includes the following: S63: using the re-established emotional state assessment model to input relevant data information; S64: Then, an accurate evaluation conclusion is obtained based on the relevant characteristic data information.

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