/Sentiment-analysis-RNN

This project deals with the basic implementation of RNN for sentiment analysis of IMDB dataset using tensorflow

Primary LanguageJupyter Notebook

Sentiment Analysis using Recurrent Neural Networks (RNN)

Introduction

This project implements sentiment analysis on the IMDB dataset using Recurrent Neural Networks (RNN) with TensorFlow. Sentiment analysis involves determining the sentiment of a given text, and in this case, we classify movie reviews as positive or negative.

Theory

Sentiment analysis is a crucial task in natural language processing and has numerous applications such as understanding customer feedback, social media monitoring, and market analysis. Recurrent Neural Networks (RNNs) are a class of neural networks particularly effective for sequential data processing tasks like sentiment analysis. RNNs are capable of capturing contextual information and dependencies within sequences, making them well-suited for tasks involving text data.

Dataset

The IMDB dataset contains 50,000 movie reviews split evenly into training and testing sets. Each review is preprocessed and encoded as a sequence of integers, with corresponding labels indicating positive or negative sentiment.

Requirements

  • Python (>=3.6)
  • TensorFlow (>=2.0)
  • NumPy
  • Pandas

Installation

  1. Clone this repository: 
    git clone https://github.com/shikhar5647/Sentiment-analysis-RNN.git