TY - JOUR ID - 1853 TI - A Novel Hierarchical Attention-based Method for Aspect-level Sentiment Classification JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Lakizadeh, A. AU - Zinaty, Z. AD - Computer Engineering Department, University of Qom, Qom, Iran. Y1 - 2021 PY - 2021 VL - 9 IS - 1 SP - 87 EP - 97 KW - deep learning KW - Sentiment Analysis KW - word embedding KW - long short-term memory DO - 10.22044/jadm.2020.9579.2091 N2 - Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to assess the sentiment polarity of each aspect in the text. However, these studies do not pay enough attention to the need for vectors to be optimal for the aspect. To address this issue, in the present study, we suggest a Hierarchical Attention-based Method (HAM) for aspect-based polarity classification of the text. HAM works in a hierarchically manner; firstly, it extracts an embedding vector for aspects. Next, it employs these aspect vectors with information content to determine the sentiment of the text. The experimental findings on the SemEval2014 data set show that HAM can improve accuracy by up to 6.74% compared to the state-of-the-art methods in aspect-based sentiment classification task. UR - https://jad.shahroodut.ac.ir/article_1853.html L1 - https://jad.shahroodut.ac.ir/article_1853_81913246131eb458cf353e46869d35c6.pdf ER -