Text sentiment analysis of fusion model based on attention mechanism

Hongjie Deng, Daji Ergu*, Fangyao Liu, Ying Cai, Bo Ma

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

31 Citations (Scopus)

Abstract

Text sentiment tendency analysis is a hot task in natural language processing. And text as the essential expression form of language, both individual word information, and overall utterance, deserves to be focused on. This paper proposes a fusion model to achieve high precision text sentiment analysis. This model combines the advantages of CNN to extract local information of text and BiLSTM to extract contextual association of text and introduces the attention mechanism to increase the focus on words with a solid emotional tendency in the text. The training datasets are comments that crawled from several social media sites such as Facebook, Twitter, Instagram, WhatsApp, etc. Based on the attention mechanism, this paper investigates the semantic sentiment analysis to reach the study of classification prediction for analyzing the positive and negative sentiment of financial news, social media, etc. The experimental results show that the proposed method can better extract features from the text and classify them than other baseline models.

Original languageEnglish
Pages (from-to)741-748
Number of pages8
JournalProcedia Computer Science
Volume199
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event8th International Conference on Information Technology and Quantitative Management, ITQM 2020 and 2021 - Chengdu, China
Duration: 9 Jul 202111 Jul 2021

Keywords

  • Attention mechanism
  • Natural language processing
  • Sentiment analysis

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