Sentiment analysis of textual data is becoming increasingly significant
in research. Many researchers are developing new technologies to
enhance the accuracy and performance of sentiment analysis. This
process is particularly vital in analysing customer reviews across
various domains. One of new domain which was explored by the
researchers is Political domain. After the inception of Smartphones
and Internet availability, various political parties are using the social
media to influence the people. As every people has their own opinion
related to political context, they always try to put it on various social
media handles like Facebook, Twitter (changed to X), Instagram,
YouTube etc. As there is lots of research and resources carried out for
few languages such as English, Chinese, Arabic but still few languages
still lag in it like Marathi, Gujrati, Telegu, Greek etc. In view of this we
have done the sentiment analysis for Marathi Tweets related to political
domain using various ML models and Word embedding techniques like
FastText, IndicNLP, Bag of Words and TF-IDF. We employed the
hyperparameter tuning to optimize each model’s performance. Among
the tested embeddings, IndicNLP proved most effective, yielding
superior accuracy and robustness across different machine learning
models, likely due to its ability to capture linguistic intricacies specific
to Indian languages. Our findings highlight the effectiveness of
advanced word embeddings like IndicNLP in sentiment analysis tasks
for under-resourced languages like Marathi, demonstrating their
potential for broader applications in regional language processing.
Swapnil P. Goje1, Rupali H. Patil2
Dr. Vishwanath Karad MIT World Peace University, India1, Shri Shivaji Vidya Prasarak Sanstha's Late Karmaveer Dr. P. R. Ghogrey Science College, India2
Sentiment Analysis, Political Tweets, Word Embeddings, Machine Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 3 , Pages: 3608 - 3617 )
Date of Publication :
January 2025
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167
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