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#3837. Low resource language specific pre-processing and features for sentiment analysis task

September 2026	publication date
Proposal available till	11-05-2025
4 total number of authors per manuscript	0 $

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Journal’s subject area:

Language and Linguistics; Linguistics and Language; Library and Information Sciences;

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Abstract:
Sentiment analysis is a classification task where polarity of textual data is identified, i.e. to analyze whether a sentence or document expresses a negative, positive or neutral sentiment. In this work, we report the sentiment analysis for a language using different types of machine learning based approaches. The dataset used in our work is collected from newspapers. The novelty of this work is that we carry out language specific pre-processing tasks such as transliteration, building negative morpheme based lexicon and filtering of noisy words. Using them as additional linguistic features in our models improves the classification result in terms of precision, recall and F-score. Based on this result, we attempt to classify the sentiment of news articles during a certain period of time. Further, we report the finding of deep learning based approaches on the same dataset.
Keywords:
BM25; Deep learning; Ensembled classifier; Low resource; Machine learning; Morphology; Pre-processing; Sentiment analysis

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