Summary of Comprehensive Study on Sentiment Analysis: From Rule-based to Modern Llm Based System, by Shailja Gupta et al.
Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system
by Shailja Gupta, Rajesh Ranjan, Surya Narayan Singh
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This comprehensive survey of sentiment analysis within artificial intelligence (AI) and large language models (LLMs) examines the historical development of sentiment analysis, highlighting the transition from traditional rule-based methods to advanced deep learning techniques. The study discusses key challenges, including handling bilingual texts, detecting sarcasm, and addressing biases. The paper reviews state-of-the-art approaches, identifies emerging trends, and outlines future research directions to advance the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey looks at how computers can understand people’s feelings about things, like movies or products. It shows how this “sentiment analysis” has changed over time, from simple rules to more complex computer programs. The paper talks about some of the tricky problems that come up when doing sentiment analysis, like dealing with texts in multiple languages or figuring out if someone is being sarcastic. It also looks at what’s currently working well and where researchers might go next. |
Keywords
» Artificial intelligence » Deep learning
