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Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
Open accessAbstract
With the increasing development of Web 2.0, such as social media and online businesses, the need for perception of opinions, attitudes, and emotions grows rapidly.Sentiment analysis, the topic studying such subjective feelings expressed in text, has attracted significant attention from both the research community and industry.Although we have known sentiment analysis as a task of mining opinions expressed in text and analyzing the entailed sentiments and emotions, so far the task is still vaguely defined in the research literature because it involves many overlapping concepts and sub-tasks.Because this is an important area of scientific research, the field needs to clear this vagueness and define various directions and aspects in detail, especially for students, scholars, and developers new to the field.In fact, the field includes numerous natural language processing tasks with different aims (such as sentiment classification, opinion information extraction, opinion summarization, sentiment retrieval, etc.) and these have multiple solution paths.Bing Liu has done a great job in this book in providing a thorough exploration and an anatomy of the sentiment analysis problem and conveyed a wealth of knowledge about different aspects of the field.
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Cited by
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis
Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.005 · Cited by 745
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
Learning to Generate Product Reviews from Attributes
Dong, L., Huang, S., Wei, F., Lapata, M., Zhou, M., & Xu, K. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · doi:10.18653/v1/e17-1059 · Cited by 190
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment
Sobhani, P., Mohammad, S., & Kiritchenko, S. (2016) · Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s16-2021 · Cited by 130
Automatic classification of literature in systematic reviews on food safety using machine learning
van den Bulk, L. M., Bouzembrak, Y., Gavai, A., Liu, N., van den Heuvel, L. J., & Marvin, H. J. P. (2022) · Current Research in Food Science · doi:10.1016/j.crfs.2021.12.010 · Cited by 38
Metadata
- Authors
- Zhao, J., Liu, K., & Xu, L.
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2016-09
- Volume
- 42
- Issue
- 3
- Pages
- 595-598
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W3088107006
- Cited by
- 552
- References
- 0
- FWCI
- 36.24
- Type
- journal article
- Language
- en
- Open access
- bronze
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