Opinion Analysis and Text Pattern Detection from Social Media Data Using Integrated Machine Learning Approach

Opinion analysis using social media data has become essential for corporations, politics, and social research because it provides insightful information about the public's attitudes toward various issues. In the realm of e-commerce, understanding consumer preferences and emerging trends is crucial for businesses to adapt and thrive in a competitive market. This paper focuses on trend detection techniques in fashion e-commerce, aiming to analyze market successes and failures. By harnessing social media data, the objective is to equip retailers with predictive tools, enabling them to anticipate trends, optimize logistics, and meet consumer demands effectively. The study utilizes Python and sentiment analysis tools, including the Vader Polarity Score, to evaluate social media content from platforms like Flipkart and Amazon. The research identifies trending keywords and assesses their relevance in the market by analyzing product names, ratings, reviews, and sentiment scores. This research empowers fashion retailers to make informed decisions by leveraging social media data and sentiment analysis. The findings spotlight current market trends and provide a framework for anticipating future consumer preferences. Implementing such techniques can revolutionize e-commerce strategies, enhance product offerings and ensure sustained market relevance.