Fine-grained sentiment classification method

Abstract

The invention relates to a fine-grained sentiment classification system and method, and the method comprises the following steps: preprocessing an inputted sentence, and mapping sentences into low-dimensional dense word vectors in a table look-up mode; and enabling a bidirectional LSTM network to perform feature extraction on the word vectors of the sentences to obtain semantic feature informationof the sentences, extract feature information of target attributes by using the semantic feature information of the sentences and an attention mechanism, perform information fusion on the feature information and the semantic feature information to obtain feature information, and perform position coding on the feature information to obtain memory information expanding the memory information by using the position information Lo to obtain network memory information Mk; extracting emotion information of the network memory information from the network memory information Mk of the target attributeby using a multi-round attention mechanism; and mapping the sentiment information into a probability vector to obtain a sentiment prediction vector, and judging a fine-grained sentiment classificationresult according to the sentiment prediction vector. Compared with the prior art, the invention can improve the network performance and improve the accuracy of fine-grained sentiment classification.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, in particular to a fine-grained emotion classification method based on target information fusion memory network. Background technique [0002] In recent years, with the rapid development of Internet technology, social media and e-commerce platforms have emerged. More and more users comment on specific commodities and events on the Internet, which makes the scale of online commentary texts grow rapidly. Sentiment analysis, also known as opinion mining, is a research field that analyzes people's subjective feelings such as opinions, emotions, evaluations, opinions, and attitudes held by entities such as products, services, organizations, individuals, events, topics, and their attributes. Text sentiment analysis has great practical value and research value. For example, identifying the emotional information of a specific product attribute from product review data can pro...

AI Technical Summary

Problems solved by technology

This makes it perform poorly in fine-grained sentiment classification tasks with multiple different targets, because the features of different sentiment words or attribute words will cancel each other out, which weakens the feature expression ability of the network

Images