Sentiment Analysis and Emojification of Tweets

– Social media platforms have become a prevalent means for individuals to share their emotionsand thoughts. With millions of tweets being posted on Twitter every day, these tweets provide us with avast dataset. Conducting sentiment analysis on this dataset can be a valuable method to obtain meaningfulinsights about societal trends. For this purpose, a sentiment analysis model and a web interface thatemojifies emotions were developed using the Python programming language. This model works on tweetsshared on Twitter and utilizes natural language processing techniques to determine the sentiment of thetweets. In this study, 168.274 English tweets were collected using the Twitter API. The collected tweetsunderwent a cleaning process where URLs, hashtags, mentions, and emojis were removed. Then, theTextBlob Python library was employed to label the tweets as positive, negative, or neutral. The labeledtweets were subjected to classification accuracy testing using Gradient Boosting, Logistic Regression,Naive Bayes, Random Forest, and Support Vector Machines machine learning models. The findingsrevealed that logistic regression achieved the highest classification accuracy with 94%. Lastly, a webinterface was developed, which retrieves the last 50 tweets of a queried user's profile and appends a relevantemoji based on the sentiment of each tweet.