Sentence subjectivity analysis of a political and ideological debate dataset using LSTM and BiLSTM with attention and GRU models

Abstract

Subjectivity analysis is one of the key tasks in the field of natural language processing. Used to annotate data as subjective or objective, subjectivity analysis can be implemented on its own or as a precursor to other NLP applications such as sentiment analysis, emotion analysis, consumer review analysis, political opinion analysis, document summarization, and question answering systems. The main objective of this article is to test and compare six deep learning methods for subjectivity classification, including Long Short-Term Memory Networks (LSTM), Gated Recurrent Units (GRU), bidirectional GRU, bidirectional LSTM, LSTM with attention, and bidirectional LSTM with attention. We introduced a combination method for subjectivity annotation using lexicon-based and syntactic pattern-based methods. We evaluated the performance of GloVe versus one-hot encoding. We also reformatted, preprocessed, and annotated a political and ideological debate dataset for use in subjectivity analysis. Our research compares favorably with the performance of existing research on subjectivity analysis, achieving very high accuracy and evaluation metrics. LSTM with attention performed the best out of all the methods we tested with an accuracy of 97.39%.