Twitter Sentiment and Sarcasm Analysis

@article{1703639,
      author = {Sarthak Tripathi, Ayushman Pandey, Anmol Chitransh, Prof S.P Medhane},
      title = {Twitter Sentiment and Sarcasm Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {1},
      pages = {251-259},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703639.pdf},
      abstract = {As we all are seeing that social media is now the biggest platform for showcasing our views and also our opinions in the support of any thought. But nowadays it is seen that most of the people didn?t get the tone of tweet and can misunderstand it. So now there is a biggest requirement to filter out the tone of our tweet i.e., we can classify our tweets into positive, negative or neutral. This project is basically based on the fact that we can use machine learning to filter out our tweets .We will extract the data from twitter and will store it in csv file, pre-process the data, then tokenize the data and using feature extraction will extract our features .Then using different machine learning algorithms such as Support Vector Machine (SVM) and Na?ve-Bayes Algorithm, polarities will be assigned to features which lies between -1 and 1.Based on value of polarity tweets will be classified as positive, negative or neutral. Machine Learning Algorithms such as Support Vector Machine, Naive Bayes Algorithm are used for sentiment classification. Support Vector Machine works mainly by investigating information and characterizing the components for calculation whereas Naive Bayes Algorithm works mainly using Bayes Theorem which is highly dependent on closeness of features. Many other tools such as Twitter Sentiment, SentiStrength etc. can be implemented but the best accuracy was given by Na?ve-Bayes Classifier.},
      month = {July},
  }