Accurate Short Text Classification using Bi-LSTM

Project Code :TCMAPY487

Objective

The Main objective of this project is to compare the performance of the models (accuracies) like Bi-LSTM, LSTM, RNN, BERT and SVM and proposes that our Bi-LSTM models outperforms all the other models in terms of accuracies.

Abstract

Large amounts of data are generated from various sources such as social media and websites. There is a need to extract meaningful information from text data, classify it into different categories, and predict end-user behavior or emotions. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. 

In this project we are using Bi-Directional long short-term memory (Bi-LSTM) model for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. Our proposed method outperforms the existing Machine Learning models with better performances

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

Block Diagram

Specifications

SOFTWARE AND HARDWARE REQUIREMENTS:

Operating system :  Windows 7 or 7+

Ram :  8 GB

Hard disc or SSD :  More than 500 GB

Processor :  Intel 3rd generation or high or Ryzen with 8 GB Ram

Software’s :  Python 3.6 or high version, Visual studio, PyCharm.


Learning Outcomes

  • What is Deep Learning?
  • Abut Deep Learning algorithms.
  • About RCNN.
  • Knowledge on PyCharm Editor.


Demo Video

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