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SOCIAL MEDIA SENTIMENT ANALYSIS

Mr. Y. Mohamed Iqbal, Dr .S. Peerbasha, K .Vijay, Dr. M. Mohamed Surputheen, Dr. T. Abdul Razak, Dr. G. Ravi, Dr. M. Kamal,

Department of Computer Science, Jamal Mohamed College, Bharathidasan University, Trichy, Tamilnadu, India.

ABSTRACT

            In today’s digital age, social media has become an integral part of daily life, influencing communication, business, and societal trends. Sentiment analysis, a subfield of Natural Language Processing (NLP), plays a vital role in understanding public opinion by extracting and analyzing subjective information from social media content. This project focuses on implementing sentiment analysis on social media platforms to classify user sentiments into positive, negative, or neutral categories. By leveraging machine learning techniques and tools like Python, Natural Language Toolkit (NLTK), and Scikit-learn, the system processes and analyzes textual data collected from social media. The project aims to assist businesses, researchers, and individuals in making informed decisions based on sentiment trends. It also demonstrates the potential of automation in handling large volumes of unstructured data efficiently and accurately.

Index Terms

            Sentiment Analysis, Natural Language Processing (NLP), Social Media, Machine Learning, Text Classification, NLTK, Scikit-learn, Opinion Mining.