Sentiment Analysis for Social Media: Using Natural Language Processing to Understand Public Opinion
DOI:
https://doi.org/10.32628/IJSRSET251238Keywords:
Social Media Data Mining, Sentiment Trend Analysis, Sarcasm Detection, Hashtag SentimentAbstract
This paper explores the operation of Natural Language Processing (NLP) techniques in sentiment analysis of social media platforms. With the exponential growth of social media as a major communication tool, understanding public sentiment has become pivotal for businesses, governments, and organizations. This study reviews various sentiment analysis techniques, challenges, and advancements in NLP, and evaluates their efficacy in understanding public opinion. The paper also highlights practical use cases of sentiment analysis in marketing, political campaigning, brand management, and customer service.
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References
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
Pak, A., & Paroubek, P. (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining.
Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis.
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis.
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