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Recently, a variety of model designs and methods have blossomed in thecontext of the sentiment analysis domain. However, there is still a lack ofwide and comprehensive studies of aspect-based sentiment analysis (ABSA). Wewant to fill this gap and propose a comparison with ablation analysis of aspectterm extraction using various text embedding methods. We particularly focusedon architectures based on long short-term memory (LSTM) with optionalconditional random field (CRF) enhancement using different pre-trained wordembeddings. Moreover, we analyzed the influence on the performance of extendingthe word vectorization step with character embedding. The experimental resultson SemEval datasets revealed that not only does bi-directional long short-termmemory (BiLSTM) outperform regular LSTM, but also word embedding coverage andits source highly affect aspect detection performance. An additional CRF layerconsistently improves the results as well.

Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings