Challenges in Multilingual and Mixed Script Sentiment Analysis

Londhe, Deepali D.; Kumari, Aruna; Emmanuel, M

doi:10.1109/i2ct51068.2021.9418087

Cited by 4 publications

(1 citation statement)

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“…The concept of low-resource languages encompasses various interpretations, including languages with limited research, facing resource scarcity, lacking computational support, being less commonly taught, or exhibiting low linguistic density [14]. In recent years, there has been a notable increase in the utilization of these languages on social media platforms, with a considerable proportion of users choosing to engage in communication through them [15]. Many languages in Africa and Asia fall into the low-resource category.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Transformer Models for Sentiment Analysis in Low-Resource Languages

Aliyu,

Sarlan,

Danyaro

et al. 2024

IJACSA

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The analysis of sentiments expressed on social media platforms is a crucial tool for understanding user opinions and preferences. The large amount of the texts found on social media are mostly in different languages. However, the accuracy of sentiment analysis in these systems faces different challenges in multilingual low-resource settings. Recent advancements in deep learning transformer models have demonstrated superior performance compared to traditional machine learning techniques. The majority of preceding works are predominantly constructed on the foundation of monolingual languages. This study presents a comparative analysis that assesses the effectiveness of transformer models, for multilingual lowresource languages sentiment analysis. The study aims to improve the accuracy of the existing baseline performance in analyzing tweets written in 12 low-resource African languages. Four widely used start-of-the-art transformer models were employed. The experiment was carried out using standard datasets of tweets. The study showcases AfriBERTa as a robust performer, exhibiting superior sentiment analysis capabilities across diverse linguistic contexts. It outperformed the established benchmarks in both SemEval-2023 Task 12 and AfriSenti baseline. Our framework achieves remarkable results with an F1-score of 81% and an accuracy rate of 80.9%. This study provides validation of the framework's robustness in the domain of sentiment analysis across a low-resource linguistics context. our research not only contributes a comprehensive sentiment analysis framework for low-resource African languages but also charts a roadmap for future enhancements. Emphasize the ongoing pursuit of adaptability and robustness in sentiment analysis models for diverse linguistic landscapes.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Transformer Models for Sentiment Analysis in Low-Resource Languages

Aliyu,

Sarlan,

Danyaro

et al. 2024

IJACSA

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SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

Kanfoud

Bouramoul

2022

J Intell Inf Syst

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The main objective of multilingual sentiment analysis is to analyze reviews regardless of the original language in which they are written. Switching from one language to another is very common on social media platforms. Analyzing these multilingual reviews is a challenge since each language is different in terms of syntax, grammar, etc. This paper presents a new language-independent representation approach for sentiment analysis, SentiCode. Unlike previous work in multilingual sentiment analysis, the proposed approach does not rely on machine translation to bridge the gap between different languages. Instead, it exploits common features of languages, such as part-of-speech tags used in Universal Dependencies. Equally important, SentiCode enables sentiment analysis in multi-language and multi-domain environments simultaneously. Several experiments were conducted using machine/deep learning techniques to evaluate the performance of SentiCode in multilingual (English, French, German, Arabic, and Russian) and multi-domain environments. In addition, the vocabulary proposed by SentiCode and the effect of each token were evaluated by the ablation method. The results highlight the 70% accuracy of SentiCode, with the best trade-off between efficiency and computing time (training and testing) in a total of about 0.67 seconds, which is very convenient for real-time applications.

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