Sentiment dictionary learning based text sentiment analysis method and system

Abstract

The invention particularly relates to a sentiment dictionary learning based text sentiment analysis method and system. The method includes steps: subjecting initial text data to normalization processing to generate preprocessed text data, and clustering the preprocessed text data to a preset field; adopting a seed sentiment dictionary based sentiment word discovery method to form a special sentiment dictionary in the preset field; subjecting the preprocessed text data to retrieval according to the special sentiment dictionary to acquire target text data serving as initial training corpus in the corresponding preset field, and classifying inputted to-be-classified texts through formed multiple classifiers. Labor cost is reduced, the problem of overfitting caused by a single classifier is avoided, and accuracy in text sentiment analysis is improved by consideration of the text related fields.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a text sentiment analysis method and system based on sentiment dictionary learning. Background technique [0002] In the Web 2.0 era, every netizen has become a source of information on the Internet. Information release platforms for various purposes emerged as the times require, such as FaceBook, Xiaonei, Sina Weibo, etc., for users to publish, obtain, and share various information. Due to the large number of Internet users, the average amount of information generated by each information release platform every day is also large, so the amount of information generated by the Internet every day is also huge. Sentiment analysis, also known as emotion mining and opinion mining, is the process of processing, analyzing, summarizing, and inferring texts to obtain the emotional color of the text. It is also very difficult. [0003] In terms of text sentiment analysis, forei...

AI Technical Summary

Problems solved by technology

The rule-based method is generally composed of manually defined rule bases and sentiment dictionaries. This method is generally effective, but the manual workload is heavy; the learning-based method is mostly based on statistical learning methods, using manually labeled corpus. Model training, the workload of this method is slightly lower than the previous one, but the effect is not ideal

Among the various methods of sentiment analysis mentioned above, the manpower and material cost of manual labeling processing is very high and there is no field-based processing, so the effect of sentiment analysis is not ideal

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