Sentiment analysis, also known as opinion mining, is the process of determining the sentiment expressed in a piece of text, whether it's positive, negative, or neutral. In this project, we implement a sentiment analysis model using logistic regression.

• 1. Logistic Regression
  • 1.1 Sigmoid
    • Exercise 1 - sigmoid (UNQ_C1)
  • 1.2 Cost function and Gradient
    • Exercise 2 - gradientDescent (UNQ_C2)
• 2. Extracting the Features
  • Exercise 3 - extract_features (UNQ_C3)
• 3. Training Your Model
• 4. Test your Logistic Regression
  • Exercise 4 - predict_tweet (UNQ_C4)
  • 4.1 Check the Performance using the Test Set
    • Exercise 5 - test_logistic_regression (UNQ_C5)
• 5. Error Analysis
• 6. Predict with your own Tweet

In this section, we implement logistic regression, a machine learning algorithm used for binary classification tasks.

We start by implementing the sigmoid function, a crucial component of logistic regression.

Next, we define the cost function and implement the gradient descent algorithm to optimize the model's parameters.

Before training our model, we need to extract features from the input data. We extract two features for sentiment analysis.

We train the logistic regression model using the extracted features and labels.

In this section, we evaluate the performance of our trained logistic regression model.

We test the model on a validation set to assess its accuracy.

We analyze the errors made by our model and explore ways to improve its performance.

Finally, we provide a way for you to use the trained model to predict the sentiment of your own tweets.

Feel free to explore the code and exercises in each section to gain a better understanding of logistic regression and sentiment analysis.