Koyel Chakraborty,Sudeshna Sani,Rajib Bag,




Machine learning,Neural Network,Sentiment Analysis,Multilingual Tweets,


India is a country of having versatile language and culture. Here, people speak in 22 different languages. With the help of Google Indic keyboard people can express their sentiments about any product, news, incidents, laws, games etc. over the social media in their native languages from individual smart phones, tablets or laptops. Sentiment analysis (SA) itself is a tough job, while multilingual SA is even harder as the style of expression varies in different languages. Among the existing approaches of SA till now the machine learning approach through neural network has overcome the limitations of others. The main aim of this paper is to represent a detailed study of the outputs generated from three different models implemented using Convolution Neural Network(CNN), Simple Recurrent Neural Network(RNN) and an amalgamated model of CNN and Long Short Term Memory (LSTM) without worrying about versatility of multilingualism using 2600 sample reviews in Hindi and Bengali. It is anticipated that the experimental results on these realistic reviews will prove to be effective for further research work.


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