Summary of Ensemble Language Models For Multilingual Sentiment Analysis, by Md Arid Hasan
Ensemble Language Models for Multilingual Sentiment Analysis
by Md Arid Hasan
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The proposed study focuses on addressing the research gap in sentiment analysis for low-resource languages like Arabic, particularly in the context of social media. Building upon advancements in sentiment analysis for commonly spoken languages, this paper investigates four pre-trained language models and proposes two ensemble models to improve performance. The study uses datasets such as SemEval-17 and the Arabic Sentiment Tweet dataset to analyze tweet texts. Results show that monolingual models outperform other approaches, while ensemble models outperform the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to fill a research gap in understanding human sentiment on social media platforms, specifically for Arabic-speaking users. By analyzing tweets using various language models and datasets, researchers can better understand how people express themselves online. The study shows that some approaches work better than others, especially when combining multiple models. |
