• A Vijay Karthik
• Dr. P.Sengottuvelan

Social analytics on the Web are now focused on sentiment detection and classification. Nowadays Twitter has turned into a significant tool for the procurement of peoples’ feelings. Sentiment analysis of tweets becomes a captivating exploration in the area of artificial intelligence. Sentiment analysis is necessary for information alignment in social media, particularly the user engagement rates are significantly higher on Twitter. Several methods have been formulated for evaluating the sentimental analysis of Twitter users, yet some improvisation is needed to improve accuracy and execution time. A major evaluation of tweets by applying efficient learning structures to accumulate the feeling of Twitter data is carried out in the proposed framework using a convolution neural network (CNN) and modified short-term memory (LSTM). Data pre-processing is performed for normalizing the data. We additionally contrasted our proposed approach and traditional AI models. Exact outcomes showed that our proposed approach results in better accuracy.

Sentiment analysis, LSTM, Convolution Neural Networks, Recurrent Neural Networks.

"SEMANTIC ANAYSIS USING DEEP LEARNING TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.f705-f716, July-2022, Available :http://www.jetir.org/papers/JETIR2207586.pdf

"SEMANTIC ANAYSIS USING DEEP LEARNING TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppf705-f716, July-2022, Available at : http://www.jetir.org/papers/JETIR2207586.pdf