Twitter Sentiment Analysis
A machine learning model for classifying the sentiment of Twitter text.
Model Details
- Task: Text Classification
- Framework: Scikit-learn
- Model Format: SAV
- Required Components: Trained model + text vectorizer
Description
This model was created as part of my machine learning learning journey, following and implementing a practical machine learning tutorial.
The project helped me practice text preprocessing, feature extraction, model training, evaluation, serialization, and integrating a machine learning model into a text classification workflow.
The repository contains two files:
trained_model.sav— the trained classification model.vectorizer.sav— the text vectorizer required to transform input text into the feature representation expected by the model.
Both files are required for inference.
Usage
import pickle
with open("vectorizer.sav", "rb") as f:
vectorizer = pickle.load(f)
with open("trained_model.sav", "rb") as f:
model = pickle.load(f)
text = ["This is an example tweet"]
X = vectorizer.transform(text)
prediction = model.predict(X)
print(prediction)
Intended Use
This model is intended for learning, experimentation, and demonstration purposes. It can be used to explore how machine learning models can be applied to text classification and sentiment analysis.
Limitations
Predictions depend on the characteristics and quality of the input text and the data used during training. Social media language can contain slang, abbreviations, sarcasm, spelling variations, and context that may affect prediction accuracy.
This model should not be considered a production-grade sentiment analysis system without additional validation and testing.
Author
Wael Gabsi
GitHub: GABSIWAEL