Fine-granularity sentiment classification method based on sentimental word random co-occurrence network

Abstract

The invention provides a fine-granularity sentiment classification method based on a sentimental word random co-occurrence network. The method comprises the steps of: forming a random network model based on a word sequence and constructed with sentiment characteristics, namely a sentimental word co-occurrence network model, by use of a random network theory and a word co-occurrence phenomenon through annotation of a sentimental noumenon vocabulary library; and carrying out model reduction on the basis, combining a sentimental word longest match (SWLM) method with a TC (Text Category) algorithm to carry out SWLM-TC unsupervised learning classification, or further combining the sentimental word longest match method with an HMM (Hidden Markov Model) machine learning algorithm to establish a fine-granularity sentiment classification model, and realizing classification prediction by use of the model. According to the method, the fine-granularity sentiment classification of a paragraph-level text can be realized, the precision of a pure TC algorithm is improved so that the classification is accurate; and after an HMM model training is carried out on a sample set by use of the SWLM-TC algorithm, the sentiment classification is carried out on a to-be-tested sample database, the automation of a pure machine learning algorithm is improved.

Description

technical field [0001] The invention belongs to the technical field of information retrieval, in particular to a fine-grained sentiment classification method based on a random co-occurrence network of sentiment words. Background technique [0002] In recent years, with the rapid development of the economy and information technology, the Internet has profoundly affected the development of the social form, and has had a huge role in promoting the economy. Internet residents have produced a vast amount of information. In the process of accelerating the landing of the mobile Internet, The popularity of various smart mobile devices allows information to spread on the Internet at a lower cost and faster speed. Different types of information will have different impacts. Negative speech will have a negative impact on netizens. Vicious group messages and The occurrence of public events will not only affect the individual's feelings, but even cause huge economic losses. Mining emotion...

AI Technical Summary

Problems solved by technology

Due to the existence of these two phenomena, sentiment analysis can simplify many things. Some studies have shown that the word co-occurrence networks established by ordinary texts in English and Chinese all satisfy the small-world characteristics, and text segmentation and topic analysis are carried out on the basis of this network. In terms of extraction, some studies use random network models for text topic analysis, and some studies use random network models for tendency analysis, but there is no relevant research on applying random network theory to text fine-grained sentiment analysis to report

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