Text Analysis Method for Students' Evaluation of Teaching Based on Sentiment Analysis Technology

Abstract

The invention discloses a text analysis method for students' teaching evaluation based on sentiment analysis technology. Mark the emotional polarity category for each teaching evaluation corpus in the collection; the teaching evaluation corpus set and category labeling constitute training samples; use the constructed training samples to train the snowNLP classifier; 3. Emotional polarity for each short sentence after sentence segmentation Classification, divided into two categories: positive emotion and negative emotion; 4. Perform part-of-speech tagging, stop word processing, and extraction of opinion words on short sentences classified as positive and negative, respectively; the extracted opinion words will appear in the original short sentence Sorting in order to get short sentences of opinions; 5. Clustering short sentences of opinions to get analysis results. This method can solve the defect of wrong classification of suggested opinion sentences by analyzing and processing students' teaching evaluation texts.

Description

technical field [0001] The invention belongs to the emotion analysis technology in the natural language processing technology, and specifically applies the emotion analysis technology to analyze the teaching evaluation texts of students. Background technique [0002] Sentiment Analysis, also known as opinion mining, is the application of text mining and computational linguistics. It is the process of identifying information from text and analyzing, processing, summarizing and reasoning subjective text with emotional color, including Sentiment polarity classification, comment summary generation, sentiment label extraction, opinion clustering, etc. [0003] At present, scholars at home and abroad have made a lot of efforts and achieved remarkable achievements in sentiment analysis, and there have been many research works that have applied sentiment analysis to student evaluation texts, such as: [0004] Tan Zheng and others used the method based on latent semantic analysis to...

AI Technical Summary

Problems solved by technology

[0007] Zhang Yanxing et al. proposed a feature-based automatic summary method for user reviews, using frequent itemset mining algorithms to extract nouns in reviews to form product features and classify reviews according to features, and finally extract sentences from them to form a summary, but it ignores the Invisible features, and many of the nouns do not belong to commodity features, that is, there are too many impurities, which will lead to problems such as wrong classification of reviews and high loss rate

Images