Project 1 (Preprocessing and Text Cleaning, Text Representation) 

Sentiment Analysis for Movie Reviews

In this activity project, we will build a sentiment analysis model to classify movie reviews as positive or negative. Sentiment analysis is a common natural language processing (NLP) task that involves determining the sentiment or opinion expressed in a piece of text. We will leverage the text representation techniques discussed in the previous lessons, namely the Bag of Words (BoW) model and the TF-IDF model, along with a machine learning algorithm, to perform sentiment analysis on movie reviews.

Project Overview:

We will use a dataset of movie reviews that are labeled as positive or negative. The goal is to build a classification model that can accurately predict the sentiment of unseen movie reviews.

Steps:

1. Data Preparation:
   • Obtain a dataset of movie reviews along with their corresponding sentiment labels. This dataset can be obtained from various sources, such as movie review websites or publicly available datasets like IMDb or Rotten Tomatoes.
   • Perform any necessary preprocessing steps on the text data, such as removing punctuation, converting text to lowercase, and handling stopwords. You can use libraries like NLTK or spaCy for these preprocessing tasks.

1. Text Representation:
   • Choose either the Bag of Words (BoW) model or the TF-IDF model to represent the movie reviews as numerical vectors.
   • If using the BoW model, implement it using the CountVectorizer class from the scikit-learn library. Fit the vectorizer on the training data and transform both the training and testing data to obtain the BoW representations.
   • If using the TF-IDF model, implement it using the TfidfVectorizer class from scikit-learn. Fit the vectorizer on the training data and transform both the training and testing data to obtain the TF-IDF representations.

1. Model Training:
   • Split the dataset into training and testing sets. A common split is to use 80% of the data for training and 20% for testing. You can use the train_test_split function from scikit-learn for this purpose.
   • Choose a machine learning algorithm for sentiment classification, such as Naive Bayes, Support Vector Machines (SVM), or a decision tree-based classifier like Random Forest or Gradient Boosting.
   • Train the chosen model on the training data, using the text representations obtained from the previous step.

1. Model Evaluation:
   • Evaluate the trained model on the testing data to measure its performance in predicting sentiment.
   • Calculate evaluation metrics such as accuracy, precision, recall, and F1-score to assess the model’s performance.
   • Analyze the confusion matrix to gain insights into the model’s strengths and weaknesses in predicting positive and negative sentiments.

1. Improvement Strategies:
   • Experiment with different parameters and configurations of the text representation models (BoW or TF-IDF) to see if it improves the model’s performance.
   • Try different machine learning algorithms and compare their performance to find the most suitable one for this task.
   • Explore the impact of using additional text processing techniques, such as n-grams or stemming/lemmatization, on the model’s performance.

1. Deployment and Application:
   • Once you are satisfied with the model’s performance, you can deploy it to predict the sentiment of new, unseen movie reviews.
   • Provide a user-friendly interface where users can input their movie reviews, and the model will predict the sentiment (positive or negative).
   • Test the model with some real-world movie reviews and assess its performance in a practical scenario.

By following these steps, you will gain hands-on experience in building a sentiment analysis model using the text representation techniques discussed in the previous lessons. This activity project will enhance your understanding of NLP tasks, text representation methods, and their application in real-world scenarios.