Sentiment Analysis Method for Social Network Text Based on Deformable Self-Attention Mechanism

Abstract

The invention discloses a social network text sentiment analysis method based on a deformable self-attention mechanism, which is used for analyzing the sentiment of user speech. The steps are as follows: segment each sentence in the user utterance text data into words, and represent each word with a word vector; input the word vector sequence into a bidirectional recurrent neural network (Bi-LSTM) to obtain the coded representation of each word; Using the deformable self-attention mechanism, the coded representation of words is converted into a variety of sentence coded representations with different context ranges; multiple sentence coded representations are fused to obtain a sentence coded representation; the fused sentence coded representation is input to the feedforward neural network Classify in the network (FFN), output the result; minimize the cross-entropy loss function iteratively train the model to update the parameters according to the model output result and the real data result; input the social network text to be classified into the trained model to obtain the sentiment analysis result.

Description

technical field [0001] The invention relates to the technical field of social network text sentiment analysis in natural language processing, in particular to a social network text sentiment analysis method based on a deformable self-attention mechanism. Background technique [0002] A platform for content production and exchange based on user relationships in the Internet is a social network, where people can share opinions with each other. Using the text sentiment analysis method, it is possible to automatically extract the user's emotional preference for some things such as products, services, events, etc., so as to help users better choose their favorite products and help businesses provide better products and services. The use of public opinion and sentiment analysis can also predict the public's attitude towards social events and the trend of emotional changes, which is conducive to enterprises and government agencies to make corresponding adjustments in real time. Se...

AI Technical Summary

Problems solved by technology

Some people use the self-attention model (see: Z.Lin, M.Feng, C.N.dos Santos, M.Yu, B.Xiang, B.Zhou, and Y.Bengio, "Astructured self-attentive sentence embedding," 2017.), however, because RNN encodes each word sequentially, it does not directly model multi-scale contextual features, and the self-attention model extracts global contextual features

Some local self-attention models also consider local context features, see: T.Shen, T.Zhou, G.Long, J.Jiang, and C.Zhang, "Tensorized self-attention: Efficiently modeling pairwise and global dependencies together ," in Proc.Conf.North Amer.ChapterAssoc.Comput.Linguistics, 2019, pp.1256–1266.), but the context features extracted by this model are also fixed-scale

At present, the methods at home and abroad are not very good at learning local context features of different scales for different words, and all of them are fixed-scale or global-scale contexts.

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