Classifying movie genres by analyzing text reviews
This is an incremental improvement for movie recommendation systems or content analysis.
The paper tackled movie genre classification using only text reviews, finding that a K-nearest neighbors model achieved the best performance with 55.4% accuracy and a Hamming loss of 0.047.
This paper proposes a method for classifying movie genres by only looking at text reviews. The data used are from Large Movie Review Dataset v1.0 and IMDb. This paper compared a K-nearest neighbors (KNN) model and a multilayer perceptron (MLP) that uses tf-idf as input features. The paper also discusses different evaluation metrics used when doing multi-label classification. For the data used in this research, the KNN model performed the best with an accuracy of 55.4\% and a Hamming loss of 0.047.