The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Sentiment analysis algorithms and applications: A survey
Open accessAbstract
Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
The Nature of Statistical Learning Theory
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Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews
Kang, H., Yoo, S. J., & Han, D. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.11.107 · Cited by 336
A random walk algorithm for automatic construction of domain-oriented sentiment lexicon
Tan, S., & Wu, Q. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.105 · Cited by 55
Cited by
Onan, A., Korukoğlu, S., & Bulut, H. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.06.005 · Cited by 367
Sentiment analysis based on improved pre-trained word embeddings
Rezaeinia, S. M., Rahmani, R., Ghodsi, A., & Veisi, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.044 · Cited by 325
Mining opinions from instructor evaluation reviews: a deep learning approach
Onan, A. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22179 · Cited by 270
Using VADER sentiment and SVM for predicting customer response sentiment
Borg, A., & Boldt, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113746 · Cited by 251
Sentiment analysis: A survey on design framework, applications and future scopes
Bordoloi, M., & Biswas, S. K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10442-2 · Cited by 183
Metadata
- Authors
- Medhat, W., Hassan, A., & Korashy, H.
- Venue
- Ain Shams Engineering Journal
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 5
- Issue
- 4
- Pages
- 1093-1113
- ISSN
- 2090-4479 (ISSN-L)
- OpenAlex
- W2019759670
- Cited by
- 3,248
- References
- 6
- FWCI
- 116.86
- Type
- journal article
- Language
- en
- Open access
- gold
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