FAST AND RELATIVELY ACCURATE SENTIMENT ANALYSIS FOR THE KAZAKH LANGUAGE

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dc.contributor.authorN. Manteyeva
dc.date.accessioned2024-01-16T04:41:05Z
dc.date.available2024-01-16T04:41:05Z
dc.date.issued2022
dc.description.abstractAbstract This paper constructs a fast and accurate sentiment analysis model for the Kazakh language. The main method for text classification is based on TF-IDF-based tokens trained with Logistic Regression. The processing and modeling stages are fully implemented in the PySpark framework. The proposed method has shown an accuracy level of 82% on an evenly distributed test dataset. As a byproduct of the work, we have collected a list of words in the Kazakh language that could signal the negativity/positivity of the given review.
dc.identifier.citationN. Manteyeva / FAST AND RELATIVELY ACCURATE SENTIMENT ANALYSIS FOR THE KAZAKH LANGUAGE / 2022 International Young Scholars' Conference
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/1125
dc.language.isoen
dc.publisher2022 International Young Scholars' Conference
dc.subjectsentiment analysis
dc.subjectnatural language processing
dc.subjectKazakh language
dc.subject2022 International Young Scholars' Conference
dc.subject№11
dc.titleFAST AND RELATIVELY ACCURATE SENTIMENT ANALYSIS FOR THE KAZAKH LANGUAGE
dc.typeOther

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