Three Executable Applications Based on Sentiment Analysis
DOI:
https://doi.org/10.61173/q4zhfh18Keywords:
Sentiment analysis, Opinion mining, NLP, Machine LearningAbstract
Sentiment analysis can be said to be one of the hottest topics in the world, and the discussion of scholars on it involves different professions and fields. In digital society, sentiment analysis as a powerful tool has gradually penetrated into various fields from theory. The purpose of this article is to propose three industries and applications where sentiment analysis can shine. The main contribution of this article is to provide enterprises with information including project feasibility, disadvantages and possible solutions.References
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
Tsytsarau, M., & Palpanas, T. (2012). Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24, 478-514.
Goldberg, A. B., & Zhu, X. (2006). Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization. In Proceedings of TextGraphs: The first workshop on graph based methods for natural language processing (pp. 45-52).
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780.
Kiritchenko S, Zhu X, Mohammad SM (2014) Sentiment analysis of short informal texts. J Artif Intell Res 50:723–762
Lee, A. M. (2024). BMW recalls more than 720,000 cars because electric water pump may catch fire. CBS News. https://www. cbsnews.com/news/bmw-recall-720000-vehicles-electric-waterpump-fire-risk-2024/
Wilson, T. (2005). Recognizing Contextual Polarity in Phrase- Level Sentiment Analysis. In Proceedings of HLT/EMNLP.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.

This work is licensed under a Creative Commons Attribution 4.0 International License.
