Classification of Elon Musk Tweets using NLP

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Classification of Elon Musk Tweets using NLP
:red_circle: Aim : Create a classification model using NLP based on the given dataset.
:red_circle: Dataset : https://www.kaggle.com/datasets/aryansingh0909/elon-musk-tweets-updated-daily
: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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• 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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• 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. 😎

[AshirMehmood]

I would like you get assigned this issue. Ashir Mehmood https://github.com/AshirMehmood Iclarkkent2001@gmail.com null To classify Elon Musk's tweets using NLP methods, I propose the following streamlined approach.

Data Collection: Gather a substantial dataset of Elon Musk's tweets, covering various topics related to Tesla, SpaceX, Neuralink, etc.

Preprocessing: Clean the tweet text by removing unnecessary characters, URLs, symbols, and emojis. Tokenize the tweets, remove stopwords, and handle noise like misspellings.

Feature Extraction: Represent tweets using Bag-of-Words (BoW) or TF-IDF to capture word frequencies and importance. Utilize pre-trained word embeddings for semantic relationships.

Model Selection: Experiment with Naive Bayes, SVM, RNN, or Transformers to find the most effective model for tweet classification.

Model Training and Evaluation: Split the dataset into training and testing sets. Train the selected model and evaluate its performance using metrics like accuracy, precision, recall, and F1-score.

Hyperparameter Tuning: Optimize the model's performance through hyperparameter tuning using techniques like grid search or random search.

Model Selection: Choose the model with the highest performance on the validation set, considering accuracy, precision, recall, and overfitting robustness. SSOC 2.0

[abhisheks008]

Issue assigned to you @AshirMehmood

[SelsabeelA]

Full name : Selsabeel Albaqir GitHub Profile Link : https://github.com/SelsabeelA/ Email ID : selsabeel.albaqir@gmail.com Approach for this Project :

EDA Data Preparation Conduct exploratory data analysis to understand the dataset's characteristics and prepare the data for modeling.

Feature Representation Experiment with various techniques such as bag-of-words, TF-IDF, and word embeddings to represent the text data as numerical features.

Model Exploration Explore multiple classification algorithms, including Naive Bayes, Support Vector Machines, Random Forest, and Neural Networks.

Model Training and Assessment Train each model on the training dataset and assess its performance using appropriate evaluation metrics and cross-validation techniques.

Optimizing Parameters Fine-tune the models and optimize their hyperparameters to improve performance.

Final Model Selection Select the best-performing model based on evaluation metrics such as accuracy, precision, recall, and F1-score.

What is your participant role? Girls' Script Summer of Code GSSOC'24 contributor

[SelsabeelA]

I would like to get assigned this issue, please. :)

[abhisheks008]

Hi @SelsabeelA thanks for showing up. As the repository name suggests, all the projects mainly focus on deep learning methods/algorithms not in basic machine learning models. Can you please rephrase your approach and put the deep learning algorithms you are planning to use here.

[SelsabeelA]

Full name : Selsabeel Albaqir GitHub Profile Link : https://github.com/SelsabeelA/ Email ID : selsabeel.albaqir@gmail.com Approach for this Project :

EDA Data Preparation: I'll begin by conducting exploratory data analysis (EDA) to understand the dataset's characteristics and preprocess the text data. This will involve steps like tokenization, removal of stopwords, lemmitization, and handling of special characters.

Feature Representation: I'll explore various word embedding techniques such as Word2Vec, GloVe, and FastText to represent the tweets as dense vectors suitable for deep learning models.

Model Exploration: I'm going to do either sentiment analysis and category analysis if time allows. I'll experiment with deep learning architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers (e.g., BERT) to build the classification model. Each architecture will be evaluated based on its sentiment classification accuracy and ability to capture semantic information.

Model Training and Assessment: I'll train each deep learning model on the training dataset and assess its performance using evaluation metrics such as accuracy, precision, recall, and F1-score. I'll employ techniques like early stopping and learning rate scheduling to prevent overfitting and improve generalization.

Optimizing Parameters: I'll optimize the hyperparameters of the deep learning models using techniques like grid search and random search to find the optimal configuration that maximizes performance on the validation set.

Final Model Selection: Based on the evaluation results, I'll select the best-performing deep learning model as the final classification model for Elon Musk tweets. I'll document the entire process, including model architectures, hyperparameters, and training strategies, to ensure reproducibility and transparency.

What is your participant role? Girls' Script Summer of Code GSSOC'24 contributor

[abhisheks008]

Hi @SelsabeelA looks good to me. Issue assigned to you. You can start working on it.

[SelsabeelA]

Small question, sorry. There's no target variable in the dataset, so is it fine if I do my own EDA and have my model explore the dataset's sentiment or categories just to perform simple data analysis instead of doing classification?

[abhisheks008]

Small question, sorry. There's no target variable in the dataset, so is it fine if I do my own EDA and have my model explore the dataset's sentiment or categories just to perform simple data analysis instead of doing classification?

No problem. Go ahead.