Experiments and results of using Natural Language Processing for Text Analysis

This paper explores the application of Natural Language Processing (NLP) techniques to analyze public sentiment and emotion within social media content, with a focus on Twitter. Two experiments are presented. he first analyzes tweets about the U.S. elections using lexicon-based labeling, clustering, and Naïve Bayes classification, revealing correlations between public sentiment and voting outcomes. The second involves building a sentiment analysis application in Python using NLTK and multiple classifiers in an ensemble system to assess sentiment polarity. Both experiments demonstrate strong classification performance and show the potential of NLP techniques for extracting meaningful insights from unstructured text. Both studies emphasize the importance of combining robust preprocessing, domain-specific lexical resources, and ensemble or hybrid machine learning strategies to ensure accurate, interpretable, and scalable sentiment analysis in real-world social media contexts.