Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network

Abstract

Genre Classification of movies is useful in the movie recommendation system for video streaming applications like Amazon, Netflix, etc. The existing methods used either video or audio data as input that requires more computation resources to process the data for the genre classification of movies. In this study, the Hierarchical Attention Neural Network (HANN) is proposed for genre classification of movies based on the social media called Twitter data as input. Twitter data related to the Telugu and English movies are collected and applied to HANN for movie’s genre classification. IMDB data are used to evaluate the performance of the proposed HANN method. The hierarchical structures of the twitter data is considered by the proposed HANN method and the most important words related to genre classification is identified by the attention mechanism, where the other neural networks such as Artificial Neural Network and Convolutional Neural Network (CNN) returns only the important weights resulting from previous words. The HANN method has the advantages of encoding the relevant information that helps to improve the performance of the recommendation system. The experimental results show that the HANN method achieve higher performance compared to other classifiers Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). The HANN method achieves accuracy of 73.15% in classification, while the existing BiLSTM method achieve the accuracy of 68% in classification.

Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison

“…Abnormal classification is a breast condition with a lump or suspicious area requiring further clinical examination with a specialist [15]. The examination serves to identify suspicious areas such as tumors or cancer [16]. This research helps radiologists and specialists accelerate the disease diagnosis process based on the acquisition of accuracy values.…”