Sentiment Analysis and Emotion Detection on Crypto currency Related Tweets Using GPT2 Model

Sentiment Analysis and Emotion Detection on Crypto currency Related Tweets Using GPT2 Model

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Product Code: Python - Machine Learning
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Product Description

Aim:

        The aim of this study is to enhance emotion analysis in the cryptocurrency market using a GPT-2 model, providing a nuanced understanding of public perceptions towards cryptocurrency transactions. This proposed system leverages the capabilities of GPT-2 to predict positive, negative, or neutral emotions associated with cryptocurrency-related tweets.


Abstract:

        The cryptocurrency market has witnessed unprecedented growth, prompting concerns and discussions about its widespread use. In this study, we focus on emotion analysis of cryptocurrency-related tweets to gain insights into public perceptions. The proposed GPT-2-enhanced system eliminates the need for LSTM and GRU models, relying solely on the advanced language generation capabilities of GPT-2. Various feature extraction methods, including term frequency-inverse document frequency, word2vec, and bag of words, are explored. Emotion analysis is conducted using lexmo.

Proposed Method:

        The proposed method introduces a GPT-2-enhanced system for emotion analysis in the cryptocurrency market. This involves utilizing the advanced language generation capabilities of GPT-2 for accurate prediction of positive, negative, or neutral emotions associated with cryptocurrency-related tweets.The proposed system eliminates the need for LSTM and GRU models, streamlining the architecture for improved efficiency.GPT-2's language model capabilities provide a more nuanced and contextually aware analysis of emotions in cryptocurrency discussions.The complex nature of GPT-2 may make it challenging to interpret and understand the decision-making process, potentially limiting the model's transparency.Dependency on pre-trained models: The performance of the GPT-2-enhanced system relies on the quality and relevance of the pre-trained GPT-2 model.


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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.