Sentiment Analysis of Comment Texts Based on BiLSTM

Abstract

With the rapid development of Internet technology and social networks, a large number of comment texts are generated on the Web. In the era of big data, mining the emotional tendency of comments through artificial intelligence technology is helpful for the timely understanding of network public opinion. The technology of sentiment analysis is a part of artificial intelligence, and its research is very meaningful for obtaining the sentiment trend of the comments. The essence of sentiment analysis is the text classification task, and different words have different contributions to classification. In the current sentiment analysis studies, distributed word representation is mostly used. However, distributed word representation only considers the semantic information of word, but ignore the sentiment information of the word. In this paper, an improved word representation method is proposed, which integrates the contribution of sentiment information into the traditional TF-IDF algorithm and generates weighted word vectors. The weighted word vectors are input into bidirectional long short term memory (BiLSTM) to capture the context information effectively, and the comment vectors are better represented. The sentiment tendency of the comment is obtained by feedforward neural network classifier. Under the same conditions, the proposed sentiment analysis method is compared with the sentiment analysis methods of RNN, CNN, LSTM, and NB. The experimental results show that the proposed sentiment analysis method has higher precision, recall, and F 1 score. The method is proved to be effective with high accuracy on comments. INDEX TERMS Sentiment analysis, artificial intelligence, social network, weighted word vectors, BiLSTM.

Sentimental Analysis using Neural Networks

“…The work by Chloe focuses on the embodied conversational agents, affect emotion, and human agent interaction and socio affective interaction [7]. In this period of expeditious development and advancement of internet and technology and social media platforms it is necessary to traverse the emotional tendencies.and the sentiment is classified using BiLSTM is quoted in this paper [8].…”

Gradual Weight Updating for Sentiment Mining

“…It further considers the words with dissimilar intensity scores also. Further G. Xu et al proposed an improved word representation method in [10]. They integrated the contribution of sentiment information into the traditional TF-IDF algorithm and generated weighted word vectors.…”

Analysis of Spatial Interaction between Different Food Cultures in South and North China: Practices from People’s Daily Life

“…In addition, it tries to construct the cognition of cultural differences between South and North China through text analysis, part-of-speech (POS) tagging, proper noun extraction, and topic word clustering using NLP. Finally, it performs emotion analysis under the influence of cultural differences between South and North China [36][37][38].…”