Learning Word Vectors for Sentiment Analysis: A Python Reproduction

A novel approach to sentiment analysis learns word vector representations that simultaneously capture both semantic similarity and sentiment polarity. The model first establishes semantic relationships by analyzing the contexts in which words appear across a large corpus of labeled and unlabeled IMDb reviews. It then injects sentiment information by using the star ratings from labeled reviews, effectively learning a "sentiment direction" within the same vector space. This combined objective function produces sentiment-aware word vectors that distinguish between words with similar meanings but opposite emotional tones, like "wonderful" and "terrible." Finally, these specialized vectors are used to build features for a linear SVM classifier to accurately predict the sentiment of movie reviews.