Accuracy of Convolution Neural Networks for Classifying Sentiments on Movie Reviews
Jeyaprakash Chelladurai, Suresh Rathnaraj Chelladurai, Biju Bajracharya · 2019
Convolution Neural Networks (CNN) models have shown remarkable results for classifying text and sentiments. In this paper, we present our approach on the task of classifying movie reviews using the word embedding on a large-scale dataset provided by Stanford. We compare the accuracy of word-based CNN models for different kernel sizes with structured and unstructured sentences. We use the standard GLoVe for word embedding and use MXNet framework to perform our experiments. Our results suggest that word orderings in a sentence do not affect the classification accuracy of CNN models significantly. However, we also note that accuracy may depend on frequency of occurrences of words.