Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight

Rong Fei,Haozheng Wu,Bo Hu,Qingzheng Xu,Yuanbo Zhu,Aimin Li,Quanzhu Yao

doi:10.1155/2020/3810261

Abstract

Within the sentiment classification field, the convolutional neural network (CNN) and long short-term memory (LSTM) are praised for their classification and prediction performance, but their accuracy, loss rate, and time are not ideal. To this purpose, a deep learning structure combining the improved cross entropy and weight for word is proposed for solving cross-domain sentiment classification, which focuses on achieving better text sentiment classification by optimizing and improving recurrent neural network (RNN) and CNN. Firstly, we use the idea of hinge loss function (hinge loss) and the triplet loss function (triplet loss) to improve the cross entropy loss. The improved cross entropy loss function is combined with the CNN model and LSTM network which are tested in the two classification problems. Then, the LSTM binary-optimize (LSTM-BO) model and CNN binary-optimize (CNN-BO) model are proposed, which are more effective in fitting the predicted errors and preventing overfitting. Finally, considering the characteristics of the processing text of the recurrent neural network, the influence of input words for the final classification is analysed, which can obtain the importance of each word to the classification results. The experiment results show that within the same time, the proposed weight-recurrent neural network (W-RNN) model gives higher weight to words with stronger emotional tendency to reduce the loss of emotional information, which improves the accuracy of classification.

Highlights

Analysis of text emotional tendency, as an important research focus in the analysis of Internet public opinion, is mainly used to analyse and process subjective information, such as attitude, emotion, viewpoint, and tendency, in text
To qualitatively and quantitatively evaluate the W-recurrent neural network (RNN) model proposed this experiment compares the effects of different models in the emotional analysis task under the Chinese and English datasets. e specific method is as follows: for the quantitative evaluation experiment, some data are selected from the Chinese and English datasets as the training set, the classification model is trained, and the emotional classification task is completed in the test set to measure the accuracy; for the qualitative evaluation experiment, the emotional weight calculated by the analysis model is to verify the validity of the model
E following results can be obtained from the above experimental results: (i) It can be seen from Table 11 that the weight-recurrent neural network (W-RNN) sentiment classification model proposed in this paper ranks the words with strong emotional tendencies in the front and gives higher weights

Summary

Introduction

Analysis of text emotional tendency, as an important research focus in the analysis of Internet public opinion, is mainly used to analyse and process subjective information, such as attitude, emotion, viewpoint, and tendency, in text. Sentiment analysis was first proposed by Pang et al [1] for the positive or negative classification of movie reviews and Turney [2] for the positive or negative classification of cars and movies in 2002. Subsequent studies on sentiment analysis have been widely carried out for hotels, restaurants, product reviews, Weibo tweets, and other fields. Traditional sentiment analysis algorithms are mostly based on shallow machine learning, such as the maximum entropy model [6], conditional random field [7], support vector machine [8], and so on. With the increasing popularity of artificial intelligence, data-driven models have gradually become a focus on research of sentiment analysis models

Methods

Results

Discussion

Conclusion