Summary of A Multilingual Sentiment Lexicon For Low-resource Language Translation Using Large Languages Models and Explainable Ai, by Melusi Malinga et al.
A Multilingual Sentiment Lexicon for Low-Resource Language Translation using Large Languages Models and Explainable AI
by Melusi Malinga, Isaac Lupanda, Mike Wa Nkongolo, Phil van Deventer
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 abstract presents a research study that aims to develop a multilingual lexicon for French and Tshiluba languages, expanding to include translations in English, Afrikaans, Sepedi, and Zulu. This lexicon enhances cultural relevance in sentiment classification by integrating language-specific sentiment scores. The study also creates a comprehensive testing corpus for translation and sentiment analysis tasks using machine learning models such as Random Forest, SVM, Decision Trees, and GNB. Notably, the Random Forest model performed well in predicting sentiment polarity and handling language-specific nuances effectively. Furthermore, BERT was applied to predict context-based sentiment with high accuracy, achieving 99% accuracy and 98% precision. The study demonstrates that the proposed lexicon and machine learning models significantly enhance translation and sentiment analysis for low-resource languages in South Africa and the DRC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about developing a special dictionary for French and Tshiluba languages to help machines understand how people feel about things. It’s like having a translator who knows different cultures and languages! The researchers created a big database of words with feelings attached to them, which helps the machine learning models make better guesses about what someone means when they say something. They even used a special computer model called BERT that’s really good at understanding language. This will help people in South Africa and the Democratic Republic of Congo communicate more easily using technology. |
Keywords
» Artificial intelligence » Bert » Classification » Machine learning » Precision » Random forest » Translation
