Gender-based multi-aspect sentiment detection using multilabel learning

Abstract

Sentiment analysis is an important task in the field of natural language processing that aims to gauge and predict people's opinions from large amounts of data. In particular, gender-based sentiment analysis can influence stakeholders and drug developers in real-world markets. In this work, we present a gender-based multi-aspect sentiment detection model using multilabel learning algorithms. We divide Abilify and Celebrex datasets into three groups based on gender information, namely: male, female, and mixed. We then represent bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and global vectors for word representation (GloVe) based features for each group. Next, we apply problem transformation approaches and multichannel recurrent neural networks with attention mechanism. Results show that traditional multilabel transformation methods achieve better performance for small amounts of data and long-range sequence in terms of samples and labels, and that deep learning models achieve better performance in terms of mean test accuracy, AUC Score, RL, and average precision using GloVe word embedding features in both datasets.