{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/targeted-aspect-based-sentiment-analysis-via","title":"Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM","arxiv_id":null,"date":"2018-04-01","proceeding":"The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) 2018 4","authors":["Yukun Ma","Haiyun Peng","Erik Cambria"],"abstract":"Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding.\r\nIn this paper, we propose a novel solution to targeted aspect-based sentiment analysis, which tackles the challenges\r\nof both aspect-based sentiment analysis and targeted sentiment analysis by exploiting commonsense knowledge. We\r\naugment the long short-term memory (LSTM) network with a hierarchical attention mechanism consisting of a target level\r\nattention and a sentence-level attention. Commonsense knowledge of sentiment-related concepts is incorporated into\r\nthe end-to-end training of a deep neural network for sentiment classification. In order to tightly integrate the commonsense\r\nknowledge into the recurrent encoder, we propose an extension of LSTM, termed Sentic LSTM. We conduct experiments\r\non two publicly released datasets, which show that the combination of the proposed attention architecture and Sentic\r\nLSTM can outperform state-of-the-art methods in targeted aspect sentiment tasks.","url_abs":"https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16541","url_pdf":"https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16541/16152","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"aspect-based-sentiment-analysis-1","task_name":"Aspect-Based Sentiment Analysis"},{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-sentihood","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"Sentihood","model":"Sentic LSTM + TA + SA","rank_in_archive_order":4,"of":5,"metrics":{"Aspect":"78.18","Sentiment":"89.32"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}