Analysis of Sentiment in Text Utilizing the Twitter Dataset

EasyChair Preprint 9749

Abstract

The computational examination of people's opinions, sentiments, attitudes, and emotions as they are represented in written language is known as sentiment analysis or opinion mining. Two factors are the key causes of its appeal. The fact that opinions are fundamental to practically all human endeavors and are significant determinants of our behavior means that it has a wide range of applications. We seek out other people's perspectives whenever we need to make a decision. Second, it poses a number of difficult research issues that had never been addressed before to the year 2000. Prior to now, there wasn't much opinionated text available in digital forms, which contributed to the lack of research. Thus, it should come as no surprise that the emergence and explosive expansion of the area parallel those of online social media. Due to its significance for industry and society at large, research on this topic has really moved beyond computer science to include management sciences and social sciences. I will introduce mainstream sentiment analysis research at the outset of this session before moving on to detail some recent work on modeling comments, discussions, and debates, which is another type of sentiment analysis and opinion analysis.

Keyphrases: LSTM, NLP, Tensor Flow, machine learning, text sentiment analysis

@booklet{EasyChair:9749,
  author    = {Bunty Prasad Nayak and Raman Chadha and Devansh Tiwari},
  title     = {Analysis of Sentiment in Text Utilizing the Twitter Dataset},
  howpublished = {EasyChair Preprint 9749},
  year      = {EasyChair, 2023}}