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Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)

Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)

Abhijit Bera, Mrinal Kanti Ghose, Dibyendu Kumar Pal
Copyright: © 2021 |Volume: 10 |Issue: 4 |Pages: 12
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781799858997|DOI: 10.4018/IJSDA.20211001.oa16
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MLA

Bera, Abhijit, et al. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)." IJSDA vol.10, no.4 2021: pp.1-12. http://doi.org/10.4018/IJSDA.20211001.oa16

APA

Bera, A., Ghose, M. K., & Pal, D. K. (2021). Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP). International Journal of System Dynamics Applications (IJSDA), 10(4), 1-12. http://doi.org/10.4018/IJSDA.20211001.oa16

Chicago

Bera, Abhijit, Mrinal Kanti Ghose, and Dibyendu Kumar Pal. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)," International Journal of System Dynamics Applications (IJSDA) 10, no.4: 1-12. http://doi.org/10.4018/IJSDA.20211001.oa16

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Abstract

Multilingual Sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages and with the help of Google Indic keyboard people can express their sentiments i.e reviews about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on Natural Language Processing (NLP) using Simple Neural Network (SNN) ,Convolutional Neural Network(CNN), and Long Short Term Memory (LSTM)Neural Network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4000 samples of reviews in English, Hindi and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.