Abstract: The data from the Twitter sentimental analysis experiment has gained much renown as a topic of research. The potential to obtain information about public opinion by breaking down Twitter data and automatically classifying its sentimental polarity has consistently attracted researchers due to the concise language commonly used in tweets. The aim of this study was to use the Valence Aware Dictionary for sEntiment Reasoner (VADER), to classify the sentiments expressed in Twitter data. In this study, we developed a generic tool for analyzing tweets. We used VADER to categorize tweets on a particular keyword to predict recent trends about it. We successfully used the generated tool to classify multilingual tweets on any keyword to analyse public opinion.
Keywords: Natural Language Processing, NLP, Twitter, sentiment analysis, VADER.