FAST AND RELATIVELY ACCURATE SENTIMENT ANALYSIS FOR THE KAZAKH LANGUAGE
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Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
2022 International Young Scholars' Conference
Abstract
Abstract 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.
Description
Keywords
sentiment analysis, natural language processing, Kazakh language, 2022 International Young Scholars' Conference, №11
Citation
N. Manteyeva / FAST AND RELATIVELY ACCURATE SENTIMENT ANALYSIS FOR THE KAZAKH LANGUAGE / 2022 International Young Scholars' Conference