Project 6: Natural Language Processing

Project 6: Natural Language Processing

Sentiment analysis program

Create a simple sentiment analysis program using NLTK and logistic regression. The program should take user input and classify it as positive or negative. Follow the steps outlined in the “Sentiment Analysis” section of the text, including data preparation, feature extraction, model training, and model evaluation. Use a labeled dataset of movie reviews or product reviews for training and testing the model. Once the model is trained and evaluated, use it to predict the sentiment of new user input. Finally, display the predicted sentiment to the user.

Guidelines:

1. Data Collection: Collect a labeled dataset of movie reviews or product reviews. You can use popular datasets like the IMDB movie reviews dataset or Amazon product reviews dataset.
2. Data Preparation: Preprocess the text data by removing unwanted characters and symbols, converting to lowercase, and tokenizing the text into individual words. You can use libraries like NLTK or spaCy for this step.
3. Feature Extraction: Convert the preprocessed text data into numerical features that can be used for training the model. Use techniques like bag-of-words, TF-IDF, or word embeddings.
4. Model Training: Train a logistic regression model on the labeled dataset. Split the dataset into training and testing sets and evaluate the model using accuracy, precision, recall, and F1 score.
5. Predicting Sentiment: Once the model is trained and evaluated, use it to predict the sentiment of new user input. Preprocess the user input using the same preprocessing techniques as before and then use the trained model to predict the sentiment.
6. Display Results: Display the predicted sentiment to the user as either positive or negative.

Here are some additional tips to keep in mind:

– Start with a small dataset and gradually increase the size as you improve the model.

– Experiment with different preprocessing techniques and feature extraction methods to see what works best for your dataset.

– Use cross-validation to ensure that your model is not overfitting to the training data.

– Use libraries like scikit-learn to make the implementation process easier and faster.