A detailed study on sentimental analysis using Twitter data with an Improved deep learning model

Under the present developments and current situation, the entire globe is changing fast. With the Internet being utilized in every sector, the internet has become a necessary requirement for everyone. With the quick expansion in informal community applications, individuals are utilizing these stages to voice their sentiments as to everyday issues. Assembling and investigating people's reactions toward purchasing an item, public administrations are essential. Sentiment analysis is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects [1]. As of late, analysts in the field of estimation examination have been worried about dissecting suppositions on various subjects, for example, films, business items, and day by day cultural issues. Twitter is a gigantically mainstream microblog on which customers may voice their assessments. Assessment examination of Twitter information is a field that has been given a lot of consideration in the course of the most recent decade and includes taking apart "tweets" and the substance of these articulations. In this paper, a deep learning model has been made with Embedding, CNN and LSTM layers. Then tweets from the web are collected for a particular topic using the Web Scraping technique by Twitter API and the overall sentiment is analyzed and a detailed sentiment report is made for that particular topic.