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Building lexicon-based sentiment analysis model for low-resource languages.

Idi Mohammed1, Rajesh Prasad1

  • 1Computer Science Department, African University of Science and Technology, Abuja, Nigeria.

Methodsx
|November 29, 2023
PubMed
Summary

This study introduces a new method for sentiment analysis in low-resource languages, achieving 98% accuracy on Hausa tweets. This approach enables Natural Language Processing (NLP) for underserved languages.

Keywords:
Building a lexicon-based sentiment analysis Model for low-resource languageFine-tuningHausa languageLexicon dictionaryLow-resource languagesSentiment analysis

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Machine Learning

Background:

  • Natural Language Processing (NLP) applications require extensive language-specific datasets, which are scarce for most of the world's over 7000 languages.
  • A significant data gap exists for Asian and African languages, hindering the development of accurate NLP tools.
  • Existing NLP resources predominantly favor a few high-resource languages like English, Chinese, and Spanish.

Purpose of the Study:

  • To propose and validate a methodology for constructing lexicon-based sentiment analysis models for low-resource languages.
  • To address the challenge of limited linguistic data in NLP development.
  • To demonstrate the applicability of the proposed method using the Hausa language.

Main Methods:

  • Developed a methodology integrating lexicon creation, augmentation, annotation, and model fine-tuning.
  • Utilized the Hausa language as a case study for training and evaluation.
  • Tested the model on a corpus of Hausa tweets.

Main Results:

  • Achieved a high accuracy of 98% on the Hausa tweet corpus.
  • Demonstrated the effectiveness of the proposed lexicon-based approach for sentiment analysis.
  • Validated the methodology's potential for broad application across low-resource languages.

Conclusions:

  • The proposed methodology offers a promising solution for sentiment analysis in languages with limited resources.
  • The approach can be adapted for various applications including social media monitoring and market research.
  • This work contributes to bridging the NLP resource gap for underrepresented languages.