Comprehensive analysis of aspect term extraction methods using various text embeddings

Abstract

Recently, a variety of model designs and methods have blossomed in the context of the sentiment analysis domain. However, there is still a lack of comprehensive studies of Aspect-based Sentiment Analysis. We want to fill this gap and propose a comparison with ablation analysis of Aspect Term Extraction using various text embeddings methods. We particularly focused on simple architectures based on long short-term memory (LSTM) with optional conditional random field (CRF) enhancement using different pre-trained word embeddings. Moreover, we analyzed the influence on the performance of extending the word vectorization step with character-based word embeddings. The experimental results on SemEval datasets revealed that bi-directional long short-term memory (BiLSTM) could be used as a very good predictor, even comparing to very sophisticated and complex models using huge word embeddings or language models. We presented a comprehensive analysis of various customizations of LSTM-based architecture and word/character embeddings that could be used as a guideline to choose the best model version for particular user needs.