Summary of Sentiment Analysis Based on Roberta For Amazon Review: An Empirical Study on Decision Making, by Xinli Guo
Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making
by Xinli Guo
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study leverages transformer-based models, specifically RoBERTa, to perform sentiment analysis on Amazon product reviews. The authors analyze a vast dataset using state-of-the-art Natural Language Processing (NLP) techniques to derive sentiment scores that accurately reflect the emotional tones of the reviews. They evaluate the performance of these models in generating sentiment scores and provide an in-depth explanation of their underlying principles. Furthermore, they conduct comprehensive data analysis and visualization to identify patterns and trends in sentiment scores, examining their alignment with behavioral economics principles such as electronic word of mouth (eWOM), consumer emotional reactions, and the confirmation bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer models to understand how people feel about products on Amazon. It looks at lots of reviews to figure out what people are really saying. The authors use a type of model called RoBERTa to do this. They show that these models can accurately measure how people feel, and they find patterns in the data that match with things we already know about consumer behavior. |
Keywords
» Artificial intelligence » Alignment » Natural language processing » Nlp » Transformer
