Sentiment Analysis on Social Media Post

Deepan Chandru

we aim to provide a scalable and accurate solution for sentiment analysis on Twitter, offering valuable insights that can influence decision-making across multiple industries.

With the rapid growth of social media platforms, the volume of user-generated content has increased significantly, offering valuable insights into public opinion and sentiment on various topics. Twitter, a leading social media platform, has emerged as a critical space where individuals express their thoughts and emotions in real-time. This project focuses on the application of sentiment analysis to Twitter data, aiming to classify tweets into positive, negative, and neutral sentiments. The primary objective is to create a robust sentiment analysis model that leverages machine learning techniques such as Logistic Regression to analyze the sentiment behind social media posts. The sentiment analysis model is trained using a dataset consisting of millions of tweets, which are pre-processed to remove noise and perform text stemming. Additionally, contextual factors such as hashtags, user mentions, and emoticons are considered to enhance the accuracy of sentiment detection. One of the challenges addressed in this study includes handling Twitter-specific nuances, such as the short length of tweets and the evolving language on the platform, including the use of slang and sarcasm. To ensure effective deployment, a web-based interface has been developed, allowing users to input text and receive real-time sentiment analysis results. This interactive website demonstrates the model’s functionality, presenting users with sentiment classifications and enabling actionable insights. The findings from this project can be applied in various domains, such as market research, brand management, political analysis, and real-time event monitoring, helping organizations understand public sentiment.

Volume 10 Issue 1, January-2025

Pages : a448-a482