{"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/review-highlights-opinion-mining-on-reviews-a","title":"Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction","arxiv_id":null,"date":"2017-10-18","proceeding":"IML ’17, October 17–18, 2017, Liverpool, United Kingdom 2017 10","authors":["Amit Kushwaha","Shubham Chaudhary"],"abstract":"This paper proposes a methodology to extract key insights from user generated reviews. This work is based on Aspect Based Sentiment Analysis (ABSA) which predicts the sentiment of aspects mentioned in the text documents. The extracted aspects are fine-grained for the presentation form known as Review Highlights.\r\n\r\nThe syntactic approach for extraction process suffers from the overlapping chunking rules which result in noise extraction. We introduce a hybrid technique which combines machine learning and rule based model. A multi-label classifier identifies the effective rules which efficiently parse aspects and opinions from texts. This selection of rules reduce the amount of noise in extraction tasks.\r\n\r\nThis is a novel attempt to learn syntactic rule fitness from a corpus using machine learning for accurate aspect extraction. As the model learns the syntactic rule prediction from the corpus, it makes the extraction method domain independent. It also allows studying the quality of syntactic rules in a different corpus.","url_abs":"https://dl.acm.org/citation.cfm?id=3158385","url_pdf":"http://vixra.org/pdf/1910.0514v1.pdf","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":[{"paper_slug":"review-highlights-opinion-mining-on-reviews-a","repo_url":"https://github.com/yardstick17/AspectBasedSentimentAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"aspect-extraction","task_name":"Aspect Extraction"},{"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":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"chunking","task_name":"Chunking"},{"task_slug":"extract-aspect","task_name":"Extract Aspect"},{"task_slug":"extract-aspect-polarity-tuple","task_name":"Extract aspect-polarity tuple"},{"task_slug":"opinion-mining","task_name":"Opinion Mining"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/extract-aspect-on-semeval-2015-task-12","task":"Extract Aspect","dataset":"SemEval 2015 Task 12","model":"EliXa","rank_in_archive_order":1,"of":2,"metrics":{"F1 score":"0.70"},"uses_additional_data":false},{"leaderboard":"/sota/extract-aspect-on-semeval-2015-task-12","task":"Extract Aspect","dataset":"SemEval 2015 Task 12","model":"Syntactic Grammar Model","rank_in_archive_order":2,"of":2,"metrics":{"F1 score":"0.63"},"uses_additional_data":false},{"leaderboard":"/sota/extract-aspect-polarity-tuple-on-semeval-2015","task":"Extract aspect-polarity tuple","dataset":"SemEval 2015 Task 12","model":"Syntactic Grammer Model","rank_in_archive_order":1,"of":1,"metrics":{"F1 score":"0.51"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}