[Feature]Analyze IMDB ratings using chatgpt and nlp

[litesh1123]

Proposal: Enhancing Sentiment Analysis Accuracy on IMDB Ratings and Comments Using ChatGPT and NLP

Project Overview The project involves extracting IMDB ratings and comments. The goal is to apply advanced Natural Language Processing (NLP) techniques and leverage ChatGPT to analyze the sentiments expressed in these comments to enhance the accuracy of sentiment classification.

Objective To improve the sentiment analysis accuracy on IMDB comments by utilizing state-of-the-art NLP models and the capabilities of ChatGPT, thus providing more reliable and nuanced sentiment insights.

Scope

1. Data Collection: gather IMDB ratings and comments.
2. Data Preprocessing: Clean and preprocess the text data to handle noise, slang, and misspellings.
3. Sentiment Analysis:
   • Baseline Model: Implement a baseline sentiment analysis model using traditional NLP techniques (e.g., Vader, TextBlob).
   • Advanced Model: Use ChatGPT and fine-tune it on a sentiment-labeled dataset for more nuanced sentiment detection.

Methodology

1. Data Preprocessing:

   • Tokenization, Lemmatization, and Stop-word Removal.
   • Handling negations, emojis, and special characters.

2. Baseline Sentiment Analysis:

   • Implement Vader and TextBlob to establish a performance baseline.

3. ChatGPT for Sentiment Analysis:

   • Fine-tune ChatGPT on a labeled sentiment dataset (e.g., IMDB sentiment dataset).
   • Apply transfer learning to adapt ChatGPT for sentiment analysis specific to movie reviews.

Tools and Technologies

• NLP Libraries: SpaCy, NLTK, , TextBlob.
• Deep Learning Frameworks: TensorFlow, PyTorch.
• Pretrained Models: OpenAI’s GPT-4, Hugging Face Transformers.
• Data Processing**: Pandas, NumPy.

Benefits

1. Improved Accuracy:

   • Leveraging advanced NLP models and ChatGPT’s capabilities can lead to more accurate sentiment analysis compared to traditional methods. This results in more reliable insights from user reviews.

2. Nuanced Understanding:

   • ChatGPT, with its ability to understand context, slang, and subtle nuances in language, can detect sentiments that traditional models might miss. This provides a deeper and more accurate analysis of user comments.

3. Enhanced User Experience:

   • By accurately analyzing sentiments, users can be presented with more relevant recommendations and insights, enhancing their overall experience on the platform.

4. Real-time Analysis:

   • The integration of an advanced sentiment analysis model enables real-time sentiment evaluation, allowing for immediate feedback and dynamic content adjustment based on user sentiments.

By utilizing ChatGPT and advanced NLP techniques, the project aims to deliver more accurate, nuanced, and actionable sentiment analysis from IMDB ratings and comments, ultimately enhancing the value derived from user-generated content.

[litesh1123]

@sanjay-kv please assign me this issue sir

[Samik123Mit]

pls, assign me the issue. I will make sure the task is done.

[litesh1123]

@Samik123Mit I have started working 50% is done Will be updating in next 2 days

[github-actions[bot]]

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