https://github.com/davidogalo/twitter-sentiment-analysis

Developed a sentiment analysis model to measure tweet positivity across regions using advanced NLP techniques. This project involved data preprocessing, feature engineering with TF-IDF and Doc2Vec, and training supervised machine learning models. Performance was validated using cross-validation and metrics like accuracy and precision
https://github.com/davidogalo/twitter-sentiment-analysis

cross-validation data-preprocessing feature-engineering machine-learning model-evaluation model-training-and-tuning natural-language-processing performance-metrics

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Developed a sentiment analysis model to measure tweet positivity across regions using advanced NLP techniques. This project involved data preprocessing, feature engineering with TF-IDF and Doc2Vec, and training supervised machine learning models. Performance was validated using cross-validation and metrics like accuracy and precision

• Host: GitHub
• URL: https://github.com/davidogalo/twitter-sentiment-analysis
• Owner: DavidOgalo
• Created: 2024-04-02T17:07:08.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-05T12:28:43.000Z (about 2 years ago)
• Last Synced: 2024-06-05T14:11:13.345Z (about 2 years ago)
• Topics: cross-validation, data-preprocessing, feature-engineering, machine-learning, model-evaluation, model-training-and-tuning, natural-language-processing, performance-metrics
• Language: Jupyter Notebook
• Homepage:
• Size: 1.13 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Sentiment Analysis on Social Media Data (Twitter)

Description

Conceptualized and developed a sentiment analysis model to quantify the positivity of tweets across diverse geographic regions. Leveraged advanced Natural Language Processing (NLP) techniques, including count vectorization, TF-IDF, and Doc2Vec, to extract meaningful insights from unstructured text data. This project involved extensive data handling and pre-processing, sophisticated machine learning algorithms, and rigorous model evaluation and validation to ensure robust and reliable performance.


Key Concepts


Data Handling and Pre-processing


> - Data Cleaning: Processed unstructured text data to handle missing values and duplicates, ensuring high-quality input for model training.  

> - Feature Engineering: Utilized count vectorization, TF-IDF, and Doc2Vec to create meaningful features from raw text data, enhancing the model's ability to understand sentiment.

> - Data Visualization: Used libraries like Seaborn and Matplotlib to visualize sentiment distribution across regions, helping to identify patterns and trends in the data.

Machine Learning Algorithms


> - Supervised Learning:  Trained the sentiment analysis model using supervised learning techniques on labeled tweet data, focusing on accurately classifying sentiment.

> - Supervised Learning:  Applied clustering methods to explore patterns in sentiment data, providing additional insights into the data's structure.

Natural Language Processing (NLP)


> - Text Pre-processing: Implemented tokenization, stemming, and lemmatization using NLTK to standardize and clean the text data, making it suitable for analysis.

> - NLP Models: Leveraged advanced models like Doc2Vec for feature extraction, capturing semantic meaning from the text data.

> - Libraries: Utilized NLTK and Gensim for various NLP tasks, ensuring robust and efficient text processing.

Model Evaluation and Validation


> - Metrics: Assessed model performance using metrics such as accuracy, precision, recall, and F1 score to ensure a comprehensive evaluation.

> - Cross-Validation: Conducted k-fold cross-validation to validate model stability and robustness, ensuring the model generalizes well to unseen data.

> - A/B Testing: Performed A/B testing to evaluate model changes and improvements, ensuring continuous enhancement of model performance.

Technologies (Tools and Libraries)





Python==3.6: Primary programming language used for the project.



NLTK==3.4.5: Used for text preprocessing tasks such as tokenization, stemming, and lemmatization.



Gensim==3.8.3: Employed for advanced NLP tasks including the implementation of Doc2Vec.



Matplotlib==3.2.1: Utilized for data visualization to explore and understand sentiment distributions.



Matplotlib==3.2.1: Utilized for data visualization to explore and understand sentiment distributions.



Seaborn==0.10.1:  Enhanced data visualization capabilities for better presentation of sentiment analysis results.



scikit-learn==0.21.3: scikit-learn: Used for machine learning model training and evaluation.



Project Breakdown


Part 1: Data Collection and Pre-processing


> - Data Collection: Gathered tweets using the Twitter API, ensuring a diverse dataset across various geographic regions. Also used a sample set from kaggle containing tweets extracted using the twitter API.

> - Data Cleaning: Processed the raw tweet data to handle missing values, duplicates, and irrelevant content.

Part 2: Feature Engineering


> - Count Vectorization: Transformed text data into numerical vectors using count vectorization.

> - TF-IDF: Applied Term Frequency-Inverse Document Frequency to weigh the importance of words in the dataset.

> - Doc2Vec: Used Doc2Vec to capture the semantic meaning of tweets, enhancing feature representation.

Part 3: Model Training and Tuning


> - Supervised Learning: Trained a sentiment analysis model using labeled data, employing algorithms like logistic regression and support vector machines.

> - Hyperparameter Tuning: Optimized model parameters to improve performance using techniques like grid search.

Part 4: Model Evaluation and Validation


> - Metrics: Evaluated model performance using accuracy, precision, recall, and F1 score.

> - Cross-Validation: Conducted k-fold cross-validation to ensure model robustness and generalizability.

> - A/B Testing: Implemented A/B testing to compare different model versions and select the best-performing model.

Getting Started



Clone the Repository

Install Dependencies: Manually install the required tools and libraries highlighted in the technologies section, versions are specified.

Dataset: Download the dataset using the Twitter API or a sample dataset from Kaggle (https://www.kaggle.com/datasets/kazanova/sentiment140) and place it in the designated directory.

Run the Preprocessing Script: Preprocess the tweets using the provided scripts to clean and standardize the data.

Feature Engineering: Execute the feature engineering scripts to transform the text data into numerical features.

Train the Model: Use the training scripts to build and optimize the sentiment analysis model.

Evaluate the Model: Run the evaluation scripts to assess the model performance using various metrics and validation techniques.



Maintainers and Contributors

Maintainer: David Ogalo 


Contributors: Contributions are welcome. Please reach out for more information on contribution guidelines on this project.