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 language | English |
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Pages (from-to) | 741-748 |
Number of pages | 8 |
Journal | Procedia Computer Science |
Volume | 199 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | 8th International Conference on Information Technology and Quantitative Management, ITQM 2020 and 2021 - Chengdu, China Duration: 9 Jul 2021 → 11 Jul 2021 |
Keywords
- Attention mechanism
- Natural language processing
- Sentiment analysis