The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Sentiment Analysis of Comment Texts Based on BiLSTM
Open accessAbstract
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 F1 score. The method is proved to be effective with high accuracy on comments.
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Cites
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Cited by
Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
Sentiment Classification Using a Single-Layered BiLSTM Model
Hameed, Z., & Garcia-Zapirain, B. (2020) · IEEE Access · doi:10.1109/access.2020.2988550 · Cited by 269
Ghimire, P., Kim, K., & Acharya, M. (2024) · Buildings · doi:10.3390/buildings14010220 · Cited by 175
Sentiment analysis in e-commerce platforms: A review of current techniques and future directions
Huang, H., Zavareh, A. A., & Mustafa, M. B. (2023) · IEEE Access · doi:10.1109/access.2023.3307308 · Cited by 117
Dewani, A., Memon, M. A., & Bhatti, S. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00550-7 · Cited by 91
Metadata
- Authors
- Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 51522-51532
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2941799245
- Cited by
- 649
- References
- 21
- FWCI
- 31.20
- Type
- journal article
- Language
- en
- Open access
- gold
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