Scalable Data processing for social media Analysis with Sentiment Analysis (KEY IJP************837)

• Bhuvanadurai M

Abstract

This project, Scalable Data Processing for Social Media Analysis with sentiment analysis, focuses on building an efficient framework to handle and analyze vast amounts of social media data for sentiment insights. The pipeline begins with data collection via APIs (e.g., Twitter, Reddit). The data is stored in scalable systems such as MongoDB, DynamoDB, or cloud storage solutions like Amazon S3 and HDFS. Preprocessing tasks like text cleaning, tokenization, and normalization are performed using Python libraries such as NLTK and SpaCy. Sentiment analysis is implemented through rule-based approaches (e.g., VADER), machine learning models, or deep learning techniques using BERT. The framework supports large-scale data processing through batch and stream processing, utilizing tools like Apache Spark, Flink, and Kafka. Finally, sentiment trends and insights are visualized using Tableau, Power BI, or Python libraries like Matplotlib. This scalable approach ensures real-time sentiment analysis for diverse applications across industries.