• P. Sravani
• N. Prathyusha
• Ch. Pavani

We introduce a novel approach for automatically classifying the sentiment of Twitter messages using NLP and deep learning. These messages are classified as either positive or negative with respect to a query term using sentiment analysis. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. There is no previous research on classifying sentiment of messages on microblogging services like Twitter. We present the results of deep learning algorithm for classifying the sentiment of Twitter messages using distant supervision. Our training data consists of Twitter messages. This type of training data is abundantly available and can be obtained through automated means. We show that deep learning algorithm LSTM which is a variant of recurrent neural network (RNN) have accuracy approximately 80% when trained with this data. This paper also describes the preprocessing steps needed in order to achieve high accuracy. The main contribution of this paper is the idea of using tweets for distant supervised learning.

Text Classification Using Different Feature Extraction Techniques

"Text Classification Using Different Feature Extraction Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.l60-l65, May-2022, Available :http://www.jetir.org/papers/JETIR2205C10.pdf

"Text Classification Using Different Feature Extraction Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppl60-l65, May-2022, Available at : http://www.jetir.org/papers/JETIR2205C10.pdf