Emotion And Sentiment Analysis From Twitter Text

Abstract

Social media like Twitter and Facebook are full of activities, emotions, and reviews from customers worldwide. Emotions are recognized improved through their adaptive use in maintaining basic services and lifestyles. Each emotion has individual characteristics: Anger, Surprise, Joy, Fear, disgust, sadness, etc. Each emotion also has characteristics in community with other emotions. We can distribute our moments, expressions, ideas, and mental state, national and international difficulties within the textual content, images, messages, and audio and video posts. Although we have different conversation types, text transmission is one of the most popular conversation techniques on social media. This paper aims to describe and understand the sentiment and emotion that humans have discovered from the text of their social media posts and apply it to generate advice. This paper collected tweet information and replies on a few special points and created a dataset with text, emotion, sentiment data, etc. We used 500 tweets data set to obtain feelings and emotions in Tweets and their re-tweets, and we covered user impact scores based on various people-based measures and tweets. The machine learning algorithm plays a vital role in sentiment analysis. In this paper, a machine learning-based Naïve Bayes classifier is used to perform emotion-relevant text classification like Anger, surprise, joy, fear, sadness, etc. The experimental results show that the proposed algorithm provides better accuracy compared to previous algorithms of KNN and DT classifiers.