Multilingual Sentiment Analysis: A Systematic Literature Review

Abstract

With the explosive growth of social media, the online community can freely express their opinions without disclosing their identities. People with hidden agendas can easily post fake opinions to discredit target products, services, politicians, or organizations. With these big data, monitoring opinions and distilling their sentiments remain a formidable task because of the proliferation of diverse sites with a large volume of opinions that are portrayed in multilingual. Therefore, this paper aims to provide a systematic literature review on multilingual sentiment analysis, which summarises the common languages supported in multilingual sentiment analysis, pre-processing techniques, existing sentiment analysis approaches, and evaluation models that have been used for multilingual sentiment analysis. By following the systematic literature review, the findings revealed, most of the models supported two languages, and English is seen as the most used language in sentiment analysis studies. None of the reviewed literature has catered the combination of languages for English, Chinese, Malay, and Hindi language on multilingual sentiment analysis. The common pre-processing techniques for the multilingual domain are tokenization, normalization, capitalization, N-gram, and machine translation. Meanwhile, the sentiment analysis classification techniques for multilingual sentiment are hybrid sentiment analysis, which includes localized language analysis, unsupervised topic clustering, and then followed by multilingual sentiment analysis. In terms of evaluation, most of the studies used precision, recall, and accuracy as the benchmark for the results.

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

“…They only highlighted a shift of research from cross-lingual to codeswitching MSA methods. Abdullah et al [39] investigated a systematic literature review from 2010 to 2019 that covered the pre-processing methods, methods for sentiment analysis, the evaluation models utilised for MSA and the aspects of common languages supported in sentiment analysis.…”

“…Lastly, Xu et al [44] investigated a systematic literature review for sentiment analysis on social media in single languages from 2018 to 2021. Comparing [5] [13] and [39] with our literature survey, there is an overlap from 2010 to 2018 but [5], [39] provides very little information about recent methods and how the MSA methods work. However, our literature survey includes prior work and the most recent year's work on African languages.…”

Comparison of Machine Learning Algorithms for Sentiment Classification on Fake News Detection

“…Sentiment analysis or opinion mining, as it is often called, is indeed one of the computational studies that discuss the analysis of opinion-oriented natural languages [6,7]. These opinion-oriented work comprises, along with other aspects, gender disparities, emotion, and attitude detection, ranks, evaluations of significance, textual perspective, description of source documents, and descriptive opinion [8].…”

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

“…They are classified into three categories: lexicon-based, machine learning (ML), and hybrid [12,24,[28][29][30][31][32][33][34][35]. However, the lexicon-based method relies on a predefined set of patterns, often referred to as a sentiment dictionary or lexicon, where each data entry is linked with a specific sentiment orientation [17,36]. The machine learning method leverages well-known ML algorithms to address sentiment analysis, treating it as a standard text classification issue that incorporates syntactic and linguistic attributes [21].…”