Sentiment Analysis and Trend Prediction in Social Media

This paper presents a research on extracting, analyzing and predicting user sentiments from various social media platforms with the help of Natural Language Processing (NLP) techniques. The project seeks to understand how people feel about the world and predict future developments within fields such as politics, entertainment and consumer trends. Event Sentiment Analysis from Data Mining based on Natural Language Processing involves the preprocessing, analysis, and classification of textual data extracted from social media sites such as Twitter, Facebook and Instagram using sophisticated NLP techniques. Some of these are tokenization, categorization of sentiment, polarity measurement, and elimination of stop words. For drawing graphs of sentiment distributions, trends, and correlations between keywords; data visualization libraries like Matplotlib, Seaborn, and Plotly are used to show more elegant representation of actual data. The predictive part employs machine learning algorithms and time series analysis to make accurate predictions and spot patterns based on past and present data.