SENTIMENT ANALYSIS OF SOCIAL MEDIA PRESENCE

Abstract

This paper presents a Sentiment Analysis of Social Media Presence system using Machine Learning (ML) and Natural Language Processing (NLP). The main objective of this work is to identify whether social media posts or comments express positive, negative, or neutral sentiments. With the rapid growth of social media platforms, people share a large number of opinions, reviews, and experiences every day. Analyzing this information manually is difficult, timeconsuming, and may produce inaccurate results. To overcome this problem, the proposed system automatically analyzes text using Machine Learning techniques. The input text is first cleaned and preprocessed using the NLTK library by removing unwanted words and symbols. Next, the TF-IDF Vectorizer converts the processed text into numerical features. The Logistic Regression algorithm is then used to classify the sentiment of the given text as positive, negative, or neutral. The system is developed using the Flask framework, which provides a simple and user-friendly interface and displays the predicted sentiment along with its confidence score. The proposed system provides a fast, accurate, and efficient solution for sentiment analysis. It can be applied in customer feedback analysis, product review analysis, brand reputation monitoring, and market research to help organizations make better decisions based on public opinion.