Emotion analysis method based on structuring features

Abstract

The invention discloses an emotion analysis method based on structuring features. The emotion analysis method includes the steps of collecting Twitter text data; building a Twitter text database; collecting existing emotion polarity value dictionaries; manually establishing related auxiliary dictionaries; preprocessing the Twitter text database; defining an emotion score influence factor, extracting language features of information, and updating the value of the emotion score influence factor every time one language feature is extracted; calculating the emotion polarity values of the Twitter text data through the emotion polarity value dictionaries and the emotion score influence factor. According to the emotion analysis method based on the structuring features, it is avoided that in supervision methods, a large amount of marked data is required to train a classifier, and analysis and generalization are difficult; the CPU processing requirement, the internal storage requirement and the overhead for calculating training time are reduced.

Description

technical field [0001] The invention relates to a sentiment analysis method. In particular, it concerns an unsupervised method for sentiment analysis based on structured features. Background technique [0002] With the emergence and popularity of social media, more and more users tend to share their unique insights or simply express their emotions and emotions through different social media platforms. Among these social platforms, Twitter has become one of the most popular websites. According to statistics in 2016, it currently has more than 645,000,000 registered users, and the average number of tweets per day exceeds 190,000,000. Through Twitter's API, we can obtain a large amount of rich data, so that we can fully detect and mine these data, which is a good opportunity for sentiment analysis. So as to help us infer the public's views on various things, and use these conclusions to make more informed predictions and choices. Sentiment analysis based on Twitter text data ...

AI Technical Summary

Problems solved by technology

[0004] The main challenges currently facing the field of sentiment analysis based on Twitter text data are mainly brought about by the characteristics of Twitter text itself: for example, the length of a tweet is limited to 140 characters, which provides us with relatively limited information; Regular language structure and grammatical expression, a tweet may also contain many acronyms, emoticons, hashtags, slang, link addresses, etc., which makes emotion extraction and opinion mining difficult

Existing commonly used traditional natural language processing technologies (Natural Language Preprocessing, NLP) such as word segmentation, standardization, part-of-speech tagging, etc. can be effectively applied to normal written normative texts, but are no longer applicable to Twitter data

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