Sentiment Analysis Using Machine Learning

This project focuses on performing sentiment analysis on textual data using classical machine learning techniques and basic Natural Language Processing (NLP). The goal is to determine whether a given text expresses a positive, negative sentiment. The model is trained on labeled text data and uses standard NLP preprocessing methods to convert raw text into machine-readable features.

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Key Features:

• 📊 Supervised Machine Learning Model:

  • Built using algorithms such as Logistic Regression, Naive Bayes, or Support Vector Machines (SVM) for classification tasks.
  • Trained on a labeled dataset of text samples annotated with sentiment labels (positive, negative).

• 🧠 Basic NLP Preprocessing:

  • Text cleaning (removing punctuation, special characters, and stopwords)
  • Tokenization and lowercasing
  • Lemmatization or stemming
  • Feature extraction using Bag of Words (BoW) or TF-IDF vectorization

• 📁 Dataset:

  • A text dataset (tweets) labeled for sentiment.
  • Split into training and testing sets for model evaluation.

• 📈 Model Evaluation:

  • Accuracy, Precision, Recall, and F1-score used to evaluate performance.
  • Confusion matrix to visualize classification results.

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Workflow Overview:

1. Data Collection: Import and clean the sentiment-labeled dataset.
2. Preprocessing: Use basic NLP techniques to prepare text data.
3. Feature Engineering: Convert text into numerical features using TF-IDF or BoW.
4. Model Training: Fit the model using a suitable ML algorithm.
5. Prediction & Evaluation: Predict sentiment on test data and evaluate performance.
6. Deployment (optional): Integrate the model into a web app using Flask for user interaction.

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Technologies Used:

• Python
• Scikit-learn
• NLTK
• Pandas, NumPy
• Matplotlib
• Flask

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