Twitter Sentiment Analysis with Machine Learning

Abstract

Sentiment analysis plays an important role in assessing the human emotions and feelings by using Natural Language Processing (NLP) technique. Researchers have recently developed different models to accurately detect and analyze the human emotions. Sentiment analysis overcomes the Natural Language Processing (NLP) challenge by using Machine Learning (ML) models to perform classification, text mining, text analysis, data analysis, and data visualization to identify positive and negative tweets. Initially, the plain text present in the tweets will be cleaned and pre-processed. The tweets are then analyzed from the pre-processed text. Followed by this, the proposed model extracts the numerical features from the data and combine them with tweet sentiments to train and detect different human sentiments. The main purpose of the proposed model is to find the offensive content in tweets. For the sake of simplicity, the proposed model considers a tweet to be vulgar content if it incorporates offensive or hateful sentiments.