Sentiment Analysis on Indonesia-English Code-Mixed Data

Utomo, Hilal Ramadhan; Romadhony, Ade

doi:10.1109/i2ct57861.2023.10126234

Cited by 2 publications

(2 citation statements)

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“…The research concludes with a comparative analysis correlating global sentiments with countries' dependence on Russian oil, achieving an accuracy of 82%. [109], found that sentiment analysis on code-mixed data faces challenges due to noise and this issue makes the conventional monolingual models less effective. They used mBERT, a multilingual pre-trained model, on English-Indonesian codemixed data, achieving the highest accuracy of 76%.…”

Section: B Pre-trained (Transformers)mentioning

confidence: 99%

“…Nonetheless, challenges persist, particularly in accurately representing all languages within tokenizer frameworks due to variations in language characters and other linguistic factors. Data cleaning [95], [35], [96], [49], [62], [65], [58], [68], [67], [89], [74], [75], [76], [77], [88], [94], [100], [103], [104], [107], [80], [106], [81], [108], [109], [82], [53], [110], [111], [83], [92], [113], [114], [115], [116], [117] Stemming [51], [67], [104], [108],…”

Section: Transfer Learningmentioning

confidence: 99%

See 1 more Smart Citation

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: B Pre-trained (Transformers)mentioning

confidence: 99%