Sentiment Analysis on Tweets

This project aims to perform sentiment analysis on tweets related to various vegetables. Using machine learning models, the sentiment of each tweet is classified as either positive or negative. The models used are: Logistic Regression, Decision Trees, KNN, Random Forest, XG Boost, SVM, and NLP (RoBERTa). This project was made originally for a Kaggle Competition, where it ended up in 1st place with a 99% accuracy.

Models Used

Several machine learning models are used to analyze the sentiment of the tweets:

1. Logistic Regression: A simple and efficient linear model for binary classification.
2. Decision Trees: A non-linear model that splits data into subsets based on feature values.
3. K-Nearest Neighbors (KNN): A non-parametric model that classifies based on the majority vote of nearest neighbors.
4. Random Forest: An ensemble model that combines multiple decision trees to improve accuracy and control over-fitting.
5. XGBoost: An optimized gradient boosting model that enhances performance and speed.
6. Support Vector Machine (SVM): A powerful model that finds the hyperplane which best separates the data into classes.
7. Natural Language Processing (NLP) with RoBERTa: A transformer-based model specifically designed for understanding the context of text. We have chosen RoBERTa over BERT as it is trained on more tweet-like data.

Classifier	BBOW Accuracy	TF-IDF Accuracy
Logistic Regression	0.929	0.932
KNN	0.733	0.582
Decision Trees	0.855	0.845
Random Forest	0.913	0.906
XG Boost	0.898	0.893
SVC	0.924	0.934
RoBERTa	0.99	0.99

Dataset

Training Dataset is expected to be a CSV file with the following structure:

• Tweet_ID: A unique integer identifying the tweet.
• Tweet: The content of the tweet.
• Sentiment: The sentiment of the tweet (1 for positive, 0 for negative).

Tweet_ID,Tweet,Sentiment
1,"I love carrots!",1
2,"I hate broccoli.",0
...

Test Dataset is expected to be a CSV file with the following structure:

• Tweet_ID: A unique integer identifying the tweet.
• Tweet: The content of the tweet.

Tweet_ID,Tweet
3,"Cauliflower is great!"
4,"I don't like spinach."
...

Prerequisites

Ensure you have the following software installed:

• Python 3.x
• Jupyter Notebook
• Necessary Python libraries (imports are in the first cell)

Model Evaluation

The models will be evaluated based on their accuracy in classifying the sentiment of tweets in the test dataset.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.