Summary of Implicit Sentiment Analysis Based on Chain Of Thought Prompting, by Zhihua Duan et al.
Implicit Sentiment Analysis Based on Chain of Thought Prompting
by Zhihua Duan, Jialin Wang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a Sentiment Analysis of Thinking (SAoT) framework, inspired by large language models’ ability to analyze implicit aspects and opinions in text. The SAoT framework analyzes implicit sentiment by first identifying common sense and thinking chain capabilities, then reflecting on the process, and finally deducing polarity. Evaluations on SemEval 2014 datasets demonstrate notable performance improvements using the ERNIE-Bot-4+SAoT model. Specifically, F1 scores reach 75.27 on restaurant reviews and 76.50 on laptop reviews, with ISA scores of 66.29 and 73.46 respectively. The model surpasses BERTAsp + SCAPt by an average margin of 47.99%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand how people feel about things without directly saying it. It’s like reading between the lines! They call this “Sentiment Analysis of Thinking” or SAoT for short. The team created a special framework that looks at text, finds hidden meanings and opinions, and then figures out if something is good or bad. They tested this on lots of restaurant and laptop reviews and found it worked really well – better than other methods actually! This can help us understand people’s feelings more accurately, which is important for things like customer service and marketing. |
