Sentiment Classification from Text Using Deep Learning Algorithm

Project Code :TCPG01

Objective

The main objective of this project is to classify the sentimental emotions from text, using BERT algorithm.

Abstract

The classification of emotional states from poetry or formal text has received less attention by the experts of computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an emotional state classification system for text is proposed using the latest and cutting edge technology of Artificial Intelligence, called Deep Learning. For this purpose, a BERT model is implemented on the text corpus. The proposed approach classifies the text into different emotional states, like neutral, joy, fear, sadness, anger, etc.

Keywords: Deep learning, emotion recognition, text, BERT, formal text, emotional states

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

Block Diagram

Specifications

H/W Configuration:

      Processor                    :    I3/Intel Processor

      Hard Disk                    :   160GB

      RAM                             :    8Gb

 

S/W Configuration:

      Operating System       :   Windows 7/8/10            .          

      IDE                                 :   Google Colab.

      Libraries Used            :    Numpy, Pandas, nltk, IO, OS.

      Technology                 :    Python 3.6+.

Learning Outcomes

  •          Practical exposure to
      •          Hardware and software tools
      •          Solution providing for real time problems
      •          Working with team/individual
      •          Work on creative ideas
  •          Testing techniques
  •          Error correction mechanisms
  •          What type of technology versions is used?
  •          Working of Tensor Flow
  •          Implementation of Deep Learning techniques
  •          Working of  BERT
  •          Building of model creations
  •          Scope of project
  •          Applications of the project
  •          About Python language
  •          About Deep Learning Frameworks
  •          Use of Data Science

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