A Review of Sentiment Analysis for Non-English Language

Djatmiko, Fahim; Ferdiana, Ridi; Faris, Muhammad

doi:10.1109/icaiit.2019.8834552

Cited by 16 publications

(12 citation statements)

References 21 publications

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“…All the challenges need to be handled properly in order to get a relevant Malay SA result. According to [26], non-English text like Indonesian also faces the same issue, lack of resources. Both machine learning and lexicon-based approaches have difficulties in terms of having a labelled data for training and dictionary which contains list of positive and negative sentiment words respectively [26].…”

Section: Discussionmentioning

confidence: 99%

“…According to [26], non-English text like Indonesian also faces the same issue, lack of resources. Both machine learning and lexicon-based approaches have difficulties in terms of having a labelled data for training and dictionary which contains list of positive and negative sentiment words respectively [26]. Other than that, there is no fully reliable techniques for pre-processed the noisy text before SA can be applied [26].…”

Section: Discussionmentioning

confidence: 99%

“…Both machine learning and lexicon-based approaches have difficulties in terms of having a labelled data for training and dictionary which contains list of positive and negative sentiment words respectively [26]. Other than that, there is no fully reliable techniques for pre-processed the noisy text before SA can be applied [26]. Same issue also faces by Urdu language where they were lacking of lexical resources with a reliable scoring mechanism [27].…”

Section: Discussionmentioning

confidence: 99%

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Sentiment Analysis of Noisy Malay Text: State of Art, Challenges and Future Work

et al. 2020

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Sentiment analysis (SA) is a study where people's opinions and emotions are automatically extracted in the form of sentiments from the natural language text. In social media monitoring, it is very useful because it allows user to gain an overall picture of the extensive public opinion behind many topics. Most works on SA are for the English text. Only a few works focus on the Malay language. Currently, a review on SA for the Malay language only focus on the SA approaches and the dataset. Some major issues such as the pre-processing techniques used to normalize the noisy text, the most employed performance measures for Malay SA, and the challenges for Malay SA has not been reviewed. Malaysians tend not to fully follow any abbreviations rules when writing on social media. Thus, a lot of noisy text can be found in social media sites like Facebook and Twitter which create some issues to SA process. Hence, the aim of this study is to investigate the state of the art, challenges and future works of SA for Malay social media text. This study provides a review on various approaches, datasets, performance measures, and pre-processing techniques used in the previous works on SA of the Malay text. More than 700 articles from journals and conference proceedings have been identified using the search keywords, however, only 17 relevant articles published from year 2013 to 2018 were reviewed. The findings from this review focus on three commonly used SA approaches which are lexicon-based, machine learning, and hybrid. INDEX TERMS Hybrid, lexicon-based, machine learning, noisy Malay text, sentiment analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Sentiment Analysis of Noisy Malay Text: State of Art, Challenges and Future Work

et al. 2020

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“…While it is evident that less research on SA is performed on languages other than English, the number of work remains significant [ 22 ]. This is especially true for Spanish, where research on SA has also seen significant advances in the past 20 years [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case

Arias

Guerra-Adames

Zambrano

et al. 2022

IJERPH

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Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.

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“…Finally, an additional benefit of our network-based approach is that it is agnostic to language outside of possessing case-specific knowledge, making it readily applicable to different contexts. While natural language processing methods have generally expanded beyond English, certain tools remain unevenly developed across languages (Djatmiko et al, 2019). In particular, sentiment analysis that is commonly used in studies of political polarization (e.g.…”

Section: Introductionmentioning

confidence: 99%

Polarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish Twittersphere

Chen¹,

Salloum²,

Gronow³

et al. 2020

Preprint

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Prior research shows that public opinion on climate politics sorts along partisan lines. However, they leave open the question of whether climate politics and other politically salient issues exhibit tendencies for issue alignment, which the political polarization literature identifies as among the most deleterious aspects of polarization. Using a network approach and social media data from the Twitter platform, we study polarization of public opinion toward climate politics and ten other politically salient topics during the 2019 Finnish elections as the emergence of opposing groups in a public forum. We find that while climate politics is not particularly polarized compared to the other topics, it is subject to partisan sorting and issue alignment within the universalist-communitarian dimension of European politics that arose following the growth of right-wing populism. Notably, climate politics is consistently aligned with the immigration issue, and temporal trends indicate that this phenomenon will likely persist.

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A Review of Sentiment Analysis for Non-English Language

Cited by 16 publications

References 21 publications

Sentiment Analysis of Noisy Malay Text: State of Art, Challenges and Future Work

Sentiment Analysis of Noisy Malay Text: State of Art, Challenges and Future Work

Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case

Polarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish Twittersphere

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