The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
A survey on sentiment analysis challenges
Open accessAbstract
With accelerated evolution of the internet as websites, social networks, blogs, online portals, reviews, opinions, recommendations, ratings, and feedback are generated by writers. This writer generated sentiment content can be about books, people, hotels, products, research, events, etc. These sentiments become very beneficial for businesses, governments, and individuals. While this content is meant to be useful, a bulk of this writer generated content require using the text mining techniques and sentiment analysis. But there are several challenges facing the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting the suitable sentiment polarity. Sentiment analysis is the practice of applying natural language processing and text analysis techniques to identify and extract subjective information from text. This paper presents a survey on the sentiment analysis challenges relevant to their approaches and techniques.
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Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05 · doi:10.3115/1220575.1220619 · Cited by 3,345
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv · doi:10.48550/arxiv.1107.4557 · Cited by 1,008
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D. Q., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., & Smith, N. A. (2011) · The Association for Computational Linguistics · Cited by 890
Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon
Mohammad, S. M., & Turney, P. D. (2010) · NPARC · Cited by 882
Sentiment Analysis of Short Informal Texts
Kiritchenko, S., Zhu, X., & Mohammad, S. M. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4272 · Cited by 880
Spotting fake reviewer groups in consumer reviews
Mukherjee, A., Liu, B., & Glance, N. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187863 · Cited by 773
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews
Guzman, E., & Maalej, W. (2014) · 2014 IEEE 22nd International Requirements Engineering Conference (RE) · doi:10.1109/re.2014.6912257 · Cited by 590
Cited by
Automating E-government services with artificial intelligence
Al-Mushayt, O. S. (2019) · IEEE Access · doi:10.1109/access.2019.2946204 · Cited by 160
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese
Bataa, E., & Wu, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1458 · Cited by 35
Chatbot Experiences of Informal Language Learners: A Sentiment Analysis
Alm, A., & Nkomo, L. M. (2022) · Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines · doi:10.4018/978-1-6684-6303-1.ch050 · Cited by 11
Metadata
- Authors
- Hussein, D. M. E.-D. M.
- Venue
- Journal of King Saud University - Engineering Sciences
- Published
- 2018-10
- Volume
- 30
- Issue
- 4
- Pages
- 330-338
- ISSN
- 1018-3639 (ISSN-L)
- OpenAlex
- W2401379394
- Cited by
- 659
- References
- 46
- FWCI
- 30.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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