Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings

Abstract

Sentiment analysis, an important task in Natural Language Processing (NLP), focuses on identifying and extracting sentiments from input. With the exponential expansion of digital information, sentiment analysis has recently gained significant attention across various domains. Traditional sentiment analysis methods paired with static embeddings often fall short in capturing the deep contextual relationships within text. In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN). We provide a comprehensive overview of the creation and assessment of a convolutional learning model especially suited for sentiment analysis of movie reviews using a dataset of around 50k entries. The proposed approach preprocesses movie reviews, employs RoBERTa to generate rich contextual embeddings, and processes these embeddings through a simple yet effective R-CNN architecture. We perform comprehensive analysis of the R-CNN model, showing a superior test accuracy of 91.5 %, achieving the best results compared to the baseline. Additionally, we develop a Flask-based application, demonstrating the practical applicability of our R-CNN model for real-time sentiment prediction.