An experimental analysis of deep neural Network based Classifiers for Sentiment Analysis Task

Project Code :TCMAPY1167

Objective

The objective of the "Experimental Analysis of Deep Neural Network-Based Classifiers for Sentiment Analysis Task" project is to assess and compare the performance of various deep neural network architectures in sentiment analysis. Through rigorous experimentation, the project aims to evaluate the effectiveness of these classifiers in accurately detecting and categorizing sentiment in text data. By examining factors such as model complexity, training data size, and feature representation methods, the project seeks to provide insights into the strengths and limitations of different neural network models for sentiment analysis applications.

Abstract

This project investigates sentiment analysis using Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) models on the Stanford Sentiment Treebank (SST-2) dataset. By exploring the efficacy of these deep learning models in interpreting binary sentiment labels from movie reviews, we aim to advance sentiment analysis techniques and inform practical applications. The system features a user-friendly interface for sentiment input, where registered users can log in, input text, and receive sentiment analysis results indicating positive or negative sentiments. This empirical evaluation seeks to compare the strengths and weaknesses of CNNs and Bi-LSTMs in capturing both local patterns and long-range dependencies in text, potentially leading to improvements in model accuracy and robustness. This project not only enhances our understanding of deep neural networks in NLP but also contributes to real-world applications by providing actionable sentiment insights.

Keywords: Sentiment analysis, CNNs, Bi-LSTM, NLP, Experimental analysis.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.