Open Access

ARTICLE

Fast Sentiment Analysis Algorithm Based on Double Model Fusion

Zhixing Lin^1,2, Like Wang^3,4, Xiaoli Cui⁵, Yongxiang Gu^3,4,*

1 Sanming University, Network Center, Sanming, 365004, China
2 College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350007, China
3 Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, 610041, China
4 University of Chinese Academy of Sciences, Beijing, 100049, China
5 Sichuan Rainbow Consulting & Software Co., Ltd., Chengdu, 610041, China

* Corresponding Author: Yongxiang Gu. Email:

Computer Systems Science and Engineering 2021, 36(1), 175-188. https://doi.org/10.32604/csse.2021.014260

Received 09 September 2020; Accepted 02 October 2020; Issue published 23 December 2020

Abstract

Nowadays, as the number of textual data is exponentially increasing, sentiment analysis has become one of the most significant tasks in natural language processing (NLP) with increasing attention. Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference. In this paper, we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization. First of all, FastText was trained to get the basic classification model, which can generate pre-trained word vectors as a by-product. Secondly, Bidirectional Long Short-Term Memory Network (Bi-LSTM) utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment analysis. By combining FastText and Bi-LSTM, we have developed a new fast sentiment analysis, called FAST-BiLSTM, which consistently achieves a balance between performance and speed. In particular, experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’ comments, and is superior to the traditional algorithm in time efficiency, accuracy, recall and F1 criteria.

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Citations