I'm proud to report that this project was awarded 93 out of 100 marks for its technical complexity and precise results. This project focused on the critical task of sentiment classification in natural language processing (NLP), specifically within the context of movie reviews.
The core objective was to design an effective model capable of distinguishing between positive and negative sentiments expressed in movie reviews. Approached this challenge by using an NLP techniques, along with rigorous data pre-processing steps.
Our primary dataset comprised of numerous user-written movie reviews, labeled as either 'positive' or 'negative'. Implemented text preprocessing steps such as tokenisation, stop words removal, and lemmatisation to reduce noise and enhance the efficiency of our models.