Opinion Analysis using Recurrent Neural Networks and Apache Spark

Opinion Analysis using Recurrent Neural Networks and Apache Spark

Mrs. A.V.L. Prasuna1, A. Sai Aakarsh2 , A. Hemanth Reddy3

1Assistant Professor, Mahatma Gandhi Institute of Technology

 2,3 UG Student, Mahatma Gandhi Institute of Technology

Abstract: The internet is full of opinions, reviews, articles, and discussions that shape decisions in business, politics, and society. But finding meaningful insights in this massive and diverse data is a big challenge. Current methods often focus on specific sources and struggle with the large scale and complexity of internet data.  This project aims to solve this problem using advanced tools like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, combined with Apache Spark for fast data processing. It can analyze different types of online content in real-time and better understand the true meaning behind words.  The results show that this approach is accurate and efficient, making it easier to uncover useful insights from online data. This system helps businesses, governments, and organizations make smarter decisions and respond quickly to what people think and feel.

Keywords: Opinion Analysis, deciphering, distributed Processing framework, Hadoop, Apache Spark, Deep Learning, Recurrent Neural Networks (RNN), sentiment score.