Machine Learning Based Sentiment Analysis of Tweets

Abstract

Sentiment Analysis is one of the computer science study areas that is expanding quickly, making it difficult to keep up with all the activity to fit in the business needs .Sentiment Analysis is used in fields of Stock Markets, Social sites like Twitter, Instagram, Facebook, elections, disasters, medicine, Sentiment Analysis in Arabic languages, software engineering to analyse the products based on the polarity scores that are classified in Positive Sentiments, Negative Sentiments and Neutral Sentiments. Here we also analysed the Sentiments of peoples outside the eco places such as park are more positive than peoples inside the park. We here used NLTK, VADER module, some deep learning modules like Roberta, Text blob and a Tweepy API. Based on the dataset utilized, the domain covered, the Arabic language type, the pre-processing techniques, the features used, the word representation, the methodology employed, and the evaluation metrics used to evaluate the suggested techniques, the included research were examined. Using the input given by Twitter popups and characterizing impressions, we present an economic analysis and responses to the wildfires that occurred in Portugal and Spain. We employ methods for machine learning technique to find the info in this text. We calculate a value for the relationship between attitudes toward wildfires and air quality and exposure are determined by Euclidean distance from the catastrophic event.