Self-Attention Networks and Adaptive Support Vector Machine for aspect-level sentiment classification

Abstract

Aspect-level sentiment classification aims to integrating the context to predict the sentiment polarity of aspect-specific in a text, which has been quite useful and popular, e.g. opinion survey and products' recommending in ecommerce. Many recent studies exploit a Long Short-Term Memory (LSTM) networks to perform aspect-level sentiment classification, but the limitation of long-term dependencies is not solved well, so that the semantic correlations between each two words of the text are ignored. In addition, traditional classification model adopts SoftMax function based on probability statistics as classifier, but ignores the words' features in the semantic space. Support Vector Machine (SVM) can fully use the information of characteristics and it is appropriate to make classification in the high dimension space, however which just considers the maximum distance between different classes and ignores the similarities between different features of the same classes. To address these defects, we propose the two-stages novel architecture named Self Attention Networks and Adaptive SVM (SAN-ASVM) for aspect-level sentiment classification. In the first-stage, in order to overcome the long-term dependencies, Multi-Heads Self Attention (MHSA) mechanism is applied to extract the semantic relationships between each two words, furthermore 1-hop attention mechanism is designed to pay more attention on some important words related to aspect-specific. In the second-stage, ASVM is designed to substitute the SoftMax function to perform sentiment classification, which can effectively make multi-classifications in high dimensional space. Extensive experiments on SemEval2014, SemEval2016 and Twitter datasets are conducted, compared experiments prove that SAN-ASVM model can obtains better performance.

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques

“…Authors mine keywords and applied feature engineering techniques to explore patterns ( Asgarnezhad, Monadjemi & Aghaei, 2022 ). Aspect-level sentiment analysis has been performed using an adaptive SVM model and Twitter dataset ( Liu et al, 2022 ). Du et al (2022) applied a gated attention model for sentiment classification.…”

A multi-feature fusion approach based on domain adaptive pretraining for aspect-based sentiment analysis

“…ABSA mainly deals with two tasks including aspect extraction (AE) and aspect-based sentiment classi cation (ASC). Aspect extraction is to extract the aspect words in the text, while aspect-based sentiment classi cation is with the purpose of identifying the sentiment polarity of the given aspect in the text (Liu et al 2022;Zhang et al 2019). The polarity of a sentence is identi ed not only by context or opinion words, but also by considering aspect words.…”