Deep neural network-based classification model for Sentiment Analysis

TL;DR

This paper develops deep neural network models, including LSTM, Bi-LSTM with attention, and CNN, to classify implicit user sentiment in social media texts, demonstrating improved accuracy over traditional DNN models.

Contribution

It introduces a Bi-LSTM with word-level attention mechanism for implicit sentiment classification, showing superior performance on public datasets.

Findings

LSTM and CNN models outperform DNN in sentiment classification

Bi-LSTM with attention achieves the highest positive sentiment identification accuracy

Models demonstrate significant improvement over existing methods

Abstract

The growing prosperity of social networks has brought great challenges to the sentimental tendency mining of users. As more and more researchers pay attention to the sentimental tendency of online users, rich research results have been obtained based on the sentiment classification of explicit texts. However, research on the implicit sentiment of users is still in its infancy. Aiming at the difficulty of implicit sentiment classification, a research on implicit sentiment classification model based on deep neural network is carried out. Classification models based on DNN, LSTM, Bi-LSTM and CNN were established to judge the tendency of the user's implicit sentiment text. Based on the Bi-LSTM model, the classification model of word-level attention mechanism is studied. The experimental results on the public dataset show that the established LSTM series classification model and CNN classification model can achieve good sentiment classification effect, and the classification effect is significantly better than the DNN model. The Bi-LSTM based attention mechanism classification model obtained the optimal R value in the positive category identification.