Project Idea: Sentiment Analysis of Movie Reviews

[mrivasperez]

Dataset: https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews

Sentiment analysis, determining whether a piece of text expresses positive, negative, or neutral sentiment, is a common task in natural language processing (NLP). This project introduces working with text data and basic NLP concepts.

Concepts

• Text Preprocessing: Raw text data needs to be cleaned and preprocessed before it can be used in a machine learning model. This involves steps like:
  - Tokenization: Splitting text into words or subword units (tokens).
  - Lowercasing: Converting all text to lowercase.
  - Removing punctuation and special characters: Cleaning up the text.
  - Handling stop words: (Optional) Removing common words like "the," "a," "is," etc., that might not carry much meaning for sentiment analysis.
• Word Embeddings: We need a way to represent words as numerical vectors. This is where word embeddings come in.
  • Pre-trained Embeddings (Word2Vec, GloVe): You can introduce the concept of using pre-trained word embeddings (like Word2Vec or GloVe), which provide vector representations of words that capture semantic relationships between them. You can use libraries like gensim to load and use these embeddings.
• Representing a Review: We need to combine the word embeddings for individual words to create a representation for the entire movie review. Simple approaches include:
  • Averaging word embeddings: Taking the average of the embeddings of all words in the review.
  • Bag-of-Words (BoW): Creating a vector where each element represents the count of a specific word in the review (ignoring word order). This can be combined with TF-IDF (Term Frequency-Inverse Document Frequency) to give more weight to important words.
  • Model Choice: You can use logistic regression (or even a simple neural network) on top of the review representation to predict sentiment.