Twitter Sentimental Analysis

@article{1702663,
      author = {Sahil Kadam, Ishani Shirsat, Akash Dwivedi, Dr. Savita Sangam},
      title = {Twitter Sentimental Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {10},
      pages = {71-75},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702663.pdf},
      abstract = {This paper address the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analyzing the sentiments expressed in the tweets. Analyzing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim of this project is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.},
      keywords = {Twitter, Social Media, Analysis, Sentiment},
      month = {April},
  }