ChatGPT Reddit Comments Analysis using NLP

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : ChatGPT Reddit Comments Analysis using NLP :red_circle: Aim : The aim of this project is to analyze the reddit comments on ChatGPT using NLP and DL methods. :red_circle: Dataset : https://www.kaggle.com/datasets/armitaraz/chatgpt-reddit :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[lakshmipriya-ragupathi]

Full name : Lakshmipriya Ragupathi GitHub Profile Link : https://github.com/lakshmipriya-ragupathi Email ID : [email protected] Participant ID (if applicable): NA Approach for this Project :

I propose the following plan:

EDA Data Preparation: Refine the comments by eliminating unnecessary characters, URLs, symbols, and emojis. Tokenize the comments, filter out stopwords, and address noise like misspellings by stemming and lemmatization

Feature Representation: Express comments using Bag-of-Words (BoW) or TF-IDF to capture word frequencies and significance. Employ pre-existing word embeddings for semantic connections.

Model Exploration: Explore Naive Bayes, SVM, RNN, or Transformers like BERT to identify the most impactful model for categorizing the comments into positive, neutral and negative ones.

Model Training and Assessment: Divide the dataset into training and testing subsets. Train the chosen model and evaluate its performance using metrics such as accuracy, precision, recall, and F1-score.

Optimizing Parameters: Enhance the model's performance through hyperparameter adjustments using methods like grid search or random search.

Final Model Selection: Select the model demonstrating the best performance on the validation set, considering accuracy, precision, recall, and its resilience against overfitting.

What is your participant role? (Mention the Open Source program)/: SWOC 2024

[abhisheks008]

Focus on Deep Learning methods rather than going to Machine Learning ones. Implement different DL methods and find out the best fitted model for this project.

Issue assigned to you @lakshmipriya-ragupathi

[Diyaa0313]

Full name : Diya Sen GitHub Profile Link : https://github.com/Diyaa0313 Email ID : [email protected] Participant ID (if applicable): NA Approach for this Project :

After using web scraping techniques to extract comments from reddit we can remove the unwanted elements to focus on the commonly used phrases which will help in the analysis. We can implement word embedding to understand the relationships between words and predict the sentiment. RNNs are generally used but there are certain limitations like the weakening of signals with each network layer. Instead, we can use LSTM and GRU. Deep Latent Dirichlet allocation can be used to get a better understanding of the data and then a combination of RNN and CNN will help us to understand the context of the comment and analyzing the sentiment.The results will be represented on charts or graphs which can help in drawing conclusions and understand the trends.

What is your participant role? GSSOC 2024 Contributor

[abhisheks008]

Assigned this to you @Diyaa0313. You can start working on it.

[github-actions[bot]]

Hello @Diyaa0313! Your issue #410 has been closed. Thank you for your contribution!