The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep learning for sentiment analysis: successful approaches and future challenges
Open accessAbstract
Sentiment analysis (also known as opinion mining) is an active research area in natural language processing. It aims at identifying, extracting and organizing sentiments from user generated texts in social networks, blogs or product reviews. A lot of studies in literature exploit machine learning approaches to solve sentiment analysis tasks from different perspectives in the past 15 years. Since the performance of a machine learner heavily depends on the choices of data representation, many studies devote to building powerful feature extractor with domain expert and careful engineering. Recently, deep learning approaches emerge as powerful computational models that discover intricate semantic representations of texts automatically from data without feature engineering. These approaches have improved the state‐of‐the‐art in many sentiment analysis tasks including sentiment classification of sentences/documents, sentiment extraction and sentiment lexicon learning. In this paper, we provide an overview of the successful deep learning approaches for sentiment analysis tasks, lay out the remaining challenges and provide some suggestions to address these challenges.WIREs Data Mining Knowl Discov 2015, 5:292–303. doi: 10.1002/widm.1171 This article is categorized under:Algorithmic Development > Text Mining Technologies > Classification Technologies > Machine Learning
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Cited by
Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
Chen, T., Xu, R., He, Y., & Wang, X. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.065 · Cited by 730
Sentiment Analysis Based on Deep Learning: A Comparative Study
Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020) · Electronics · doi:10.3390/electronics9030483 · Cited by 593
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges
Das, R., & Singh, T. D. (2023) · ACM Computing Surveys · doi:10.1145/3586075 · Cited by 363
A survey on sentiment analysis and opinion mining for social multimedia
Li, Z., Fan, Y., Jiang, B., Lei, T., & Liu, W. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6445-z · Cited by 164
Deep learning for online harassment detection in tweets
Marwa, T., Salima, O., & Souham, M. (2018) · 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) · doi:10.1109/pais.2018.8598530 · Cited by 35
Metadata
- Authors
- Tang, D., Qin, B., & Liu, T.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2015-10-23
- Volume
- 5
- Issue
- 6
- Pages
- 292-303
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W2146338426
- Cited by
- 207
- References
- 9
- FWCI
- 9.70
- Type
- journal article
- Language
- en
- Open access
- green
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