Exploration of Sentiment Analysis Techniques on a Multilingual Dataset Dealing with Tamil-English reviews

Sangeetha, M.; Nimala, K.

doi:10.1109/accai53970.2022.9752612

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…The collection of several languages' text that may be categorized as positive, negative, or neutral polarity is termed a multilingual sentiment analysis [13]. Notably, sentiment analysis is becoming more significant in languages other than English.…”

Section: Introductionmentioning

confidence: 99%

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

Aliyu,

Sarlan,

Usman Danyaro

et al. 2024

IEEE Access

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The field of low-resource sentiment analysis has seen significant developments in recent years. This research review SLR evaluates the approaches and data sources utilized in low-resource sentiment analysis by deep learning. The primary aim is to discover suitable approaches for future sentiment analysis in low-resource. Our studies explore various languages, models, and data sources expressing a desire to create effective approaches. Our emphasis lies in the critical evaluation of the approaches and the datasets utilized, to identify areas where further research is needed. Our analysis study adds to the existing body of literature reviews, encompassing multilingual low-resource sentiment analysis research spanning from 2018 to 2023. The findings indicate that the transfer learning approach is the most frequently used, followed by word embedding learning and machine translation systems. Additionally, the study shows that social media is the most used platform for data collection, followed by product reviews, movies, and hotels. There has been a significant surge in the adoption of pre-trained transformers, indicating a growing interest in exploring the potential of these models for low-resource languages within the natural language processing (NLP) community. This trend is largely attributed to the novel nature of these models and their feature of being nonlabour intensive. However, the scarcity of annotated datasets for such languages remains a major hurdle. finally, these research findings are relevant and informative for any researcher working in the field of lowresource multilingual sentiment analysis. The study introduces a conceptual framework for performing sentiment analysis in low-resource. The study provides a valuable resource for future researchers.

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Section: Introductionmentioning

confidence: 99%

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

Aliyu,

Sarlan,

Usman Danyaro

et al. 2024

IEEE Access

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show abstract

“…To build a comprehensive corpus for a regional language, such as Tamil, an extensive range of text sources including books, articles, websites and social media posts are used. Corpus reflects the diverse vocabulary, grammar, and usage patterns of the language [1]. In NLP and regional languages cleaned and pre-processed.…”

Section: Introductionmentioning

confidence: 99%

Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages

2024

JMC

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Review comments from digital platform such as Facebook, Twitter and YouTube used for identification of emotional tones from text. Nowadays, reviews are posted in different languages such as English, French, Chinese, and Indian regional languages such as Tamil, Telegu, and Hindi. Identification of emotional tones from text written in Indian regional language is challenging. During the translation of the regional language to the English language for sentiment analysis, lexical and pragmatic ambiguity are the major problem. The above problem arises due to dialects in language such as regional, standard, and social dialects. In this paper, dialect-based ambiguity problems solve through proposed Hybrid optimized deep learning transformer Models like M-BERT, M-Roberta, and M-XLM-Roberta for Tamil language dialects recognise and classified. The proposed algorithms provide better sentimental analysis after Hybrid optimization due to adaptation mechanisms, dynamic changes in the parameters and strategies in fine-tuning the search. The proposed Hybrid optimized algorithms perform better than existing algorithms such as SVM, Naïve Bayes, and LSTM with an accuracy of 95%.

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“…To address the existing gap in the field, this study introduces a code-mixed and sentiment-annotated corpus in both Tamil and English languages. A unique feature of this dataset is its inclusion of comments that seamlessly blend Tamil and English in varying proportions, mirroring the complexities of real-world code-mixed communication [5].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on Code-Mixed Tamil-English Corpus: A Comprehensive Study of Transformer-Based Models

Sangeetha,

Nimala

2023

Preprint

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NLP, or natural language processing, is a subfield of AI that aims to equip computers with the ability to understand and analyze human language. Sentiment analysis is a widely used application of NLP, particularly for examining attitudes expressed in online conversations. Nevertheless, many social media comments are written in languages that are not native to the authors, making sentiment analysis more difficult, especially for languages with limited resources, such as Tamil. To tackle this issue, a code-mixed and sentiment-annotated corpus in Tamil and English was created. This article will explain how the corpus was established, including the process of data collection and the assignment of polarities. The article will also explore the agreement between annotators and the results of sentiment analysis performed on the corpus. This work signifies various performance metrics such as precision, recall, support, and F1-score for the transformer-based model such as BERT, RoBerta, and XLM-RoBerta. Among the various models, XLM-Robert shows slightly significant positive results on the code-mixed corpus when compared to the state of art models.

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Exploration of Sentiment Analysis Techniques on a Multilingual Dataset Dealing with Tamil-English reviews

Cited by 9 publications

References 8 publications

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

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

Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages

Sentiment Analysis on Code-Mixed Tamil-English Corpus: A Comprehensive Study of Transformer-Based Models

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