Authors: Srujan Gatla, Teja, Surya, Shashi Kumar
Abstract: Sentiment analysis on social media is a natural language processing technique used to extract subjective information and opinions from user-generated content on various social media platforms, such as Twitter, Facebook, and Instagram. The goal of this project is to perform sentiment analysis on social media data related to a particular topic or brand, such as a product launch or a social issue. Social media data will be collected using relevant APIs or web scraping tools and preprocessed by cleaning and filtering out irrelevant or spammy content. A sentiment analysis model, such as a lexicon-based or machine learning model, will be applied to classify the sentiment of the content as positive, negative, or neutral. Results will be visualized and analysed using various techniques and tools, such as word clouds, bar charts, and time series analysis, to gain insights and make data-driven decisions based on public opinion. Challenges in social media sentiment analysis include the use of slang, emojis, and hashtags, as well as the need to handle multilingual con tent. Sentiment analysis on social media can be a powerful tool for understanding public opinion, customer satisfaction, and brand reputation, and making data-driven deci- sions in various domains, such as marketing, politics, and social sciences.
