{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:15:06Z","timestamp":1754158506240,"version":"3.41.2"},"reference-count":42,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2016,6,6]],"date-time":"2016-06-06T00:00:00Z","timestamp":1465171200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EL"],"published-print":{"date-parts":[[2016,6,6]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Opinion mining (OM), also known as \u201csentiment classification\u201d, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the \u201cis-a\u201d relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>To demonstrate the \u201cfeature-based OM using topic maps\u201d applications, this work implements a prototype that helps users to find their new washing machines.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/el-11-2014-0197","type":"journal-article","created":{"date-parts":[[2016,6,1]],"date-time":"2016-06-01T17:05:35Z","timestamp":1464800735000},"page":"435-456","source":"Crossref","is-referenced-by-count":2,"title":["Research on feature-based opinion mining using topic maps"],"prefix":"10.1108","volume":"34","author":[{"given":"Lixin","family":"Xia","sequence":"first","affiliation":[]},{"given":"Zhongyi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Zhai","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"14","key":"key2020121500495150100_ref001","first-page":"1269","article-title":"Intelligent feature selection for opinion classification","volume":"54","year":"2003","journal-title":"Technology"},{"year":"2010","article-title":"SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining","key":"key2020121500495150100_ref002"},{"year":"2008","first-page":"14","article-title":"Building a sentiment summarizer for local service reviews","key":"key2020121500495150100_ref003"},{"year":"2005","first-page":"355","article-title":"Identifying sources of opinions with conditional random fields and extraction patterns","key":"key2020121500495150100_ref004"},{"year":"2010","first-page":"13","article-title":"A knowledge-rich approach to feature-based opinion extraction from product reviews","key":"key2020121500495150100_ref005"},{"year":"2003","first-page":"519","article-title":"Mining the peanut gallery: opinion extraction and semantic classification of product reviews","key":"key2020121500495150100_ref006"},{"year":"2007","first-page":"811","article-title":"The utility of linguistic rules in opinion mining","key":"key2020121500495150100_ref007"},{"year":"2008","first-page":"231","article-title":"A holistic lexicon-based approach to opinion mining","key":"key2020121500495150100_ref008"},{"year":"2005","first-page":"617","article-title":"Determining the semantic orientation of terms through gloss classification","key":"key2020121500495150100_ref009"},{"year":"2007","first-page":"416","article-title":"Opinion mining using econometrics: a case study on reputation systems","key":"key2020121500495150100_ref010"},{"issue":"2","key":"key2020121500495150100_ref011","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1006\/knac.1993.1008","article-title":"A translation approach to portable ontology specifications","volume":"5","year":"1993","journal-title":"Knowledge Acquisition"},{"issue":"5","key":"key2020121500495150100_ref012","first-page":"907","article-title":"Toward principles for the design of ontologies used for knowledge sharing?","volume":"43","year":"1995","journal-title":"International Journal of Human-Computer Studies"},{"year":"1997","first-page":"174","article-title":"Predicting the semantic orientation of adjectives","key":"key2020121500495150100_ref013"},{"year":"2004","first-page":"168","article-title":"Mining and summarizing customer reviews","key":"key2020121500495150100_ref014"},{"year":"2004","first-page":"755","article-title":"Mining opinion features in customer reviews","key":"key2020121500495150100_ref015"},{"year":"2006","first-page":"355","article-title":"Fully automatic lexicon expansion for domain-oriented sentiment analysis","key":"key2020121500495150100_ref016"},{"year":"2004","first-page":"1367","article-title":"Determining the sentiment of opinions","key":"key2020121500495150100_ref017"},{"unstructured":"Liu, B. (2010), \u201cSentiment analysis and subjectivity\u201d, available at: www.cs.uic.edu\/\u223cliub\/FBS\/NLP-handbook-sentiment-analysis.pdf (accessed 31 January 2016).","key":"key2020121500495150100_ref018"},{"year":"2005","first-page":"342","article-title":"Opinion observer: analyzing and comparing opinions on the web","key":"key2020121500495150100_ref019"},{"year":"2000","first-page":"483","article-title":"An environment for merging and testing large ontologies","key":"key2020121500495150100_ref020"},{"key":"key2020121500495150100_ref021","first-page":"573","article-title":"Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis","volume-title":"Advances in Information Retrieval","year":"2007"},{"unstructured":"Members of the TopicMaps.Org Authoring Group (2001), \u201cXML Topic Maps (XTM) 1.0\u201d, available at: www.topicmaps.org\/xtm\/ (accessed 12 October 2014).","key":"key2020121500495150100_ref022"},{"issue":"5","key":"key2020121500495150100_ref023","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/0010-4809(92)90003-S","article-title":"Dimensions of knowledge sharing and reuse","volume":"25","year":"1992","journal-title":"Computers and Biomedical Research"},{"year":"2003","first-page":"70","article-title":"Sentiment analysis: capturing favorability using natural language processing","key":"key2020121500495150100_ref024"},{"issue":"1","key":"key2020121500495150100_ref025","first-page":"1","article-title":"Opinion mining and sentiment analysis","volume":"2","year":"2008","journal-title":"Foundations and Trends in Information Retrieval"},{"year":"2002","first-page":"79","article-title":"Thumbs up?: sentiment classification using machine learning techniques","key":"key2020121500495150100_ref026"},{"key":"key2020121500495150100_ref027","first-page":"145","article-title":"Just for me: topic maps and ontologies","volume-title":"Charting the Topic Maps Research and Applications Landscape","year":"2006"},{"key":"key2020121500495150100_ref028","first-page":"5247","article-title":"Topic maps","volume-title":"Encyclopedia of Library and Information Sciences","year":"2010"},{"year":"2005","first-page":"339","article-title":"Extracting product features and opinions from reviews","key":"key2020121500495150100_ref029"},{"key":"key2020121500495150100_ref030","first-page":"14","article-title":"Metamorphosis \u2013 a topic maps based environment to handle heterogeneous information resources","volume-title":"Charting the Topic Maps Research and Applications Landscape","year":"2006"},{"issue":"3","key":"key2020121500495150100_ref031","first-page":"303","article-title":"Reusable ontologies, knowledge-acquisition tools, and performance systems: PROT\u00c9G\u00c9-II solutions to Sisyphus-2","volume":"44","year":"1996","journal-title":"International Journal of Human-Computer Studies"},{"year":"2007","first-page":"182","article-title":"Red Opal: product-feature scoring from reviews","key":"key2020121500495150100_ref032"},{"year":"2002","first-page":"417","article-title":"Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews","key":"key2020121500495150100_ref033"},{"issue":"4","key":"key2020121500495150100_ref034","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1145\/944012.944013","article-title":"Measuring praise and criticism: inference of semantic orientation from association","volume":"21","year":"2003","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"year":"2004","first-page":"18","article-title":"Ontology-driven topic maps","key":"key2020121500495150100_ref035"},{"issue":"9","key":"key2020121500495150100_ref036","first-page":"23","article-title":"Research directions in social network mining with empirical study on opinion mining","volume":"37","year":"2013","journal-title":"CSI Communication"},{"key":"key2020121500495150100_ref037","first-page":"486","article-title":"Creating subjective and objective sentence classifiers from unannotated texts","volume-title":"Computational Linguistics and Intelligent Text Processing","year":"2005"},{"issue":"3","key":"key2020121500495150100_ref038","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1162\/0891201041850885","article-title":"Learning subjective language","volume":"30","year":"2004","journal-title":"Computational Linguistics"},{"year":"2005","first-page":"347","article-title":"Recognizing contextual polarity in phrase-level sentiment analysis","key":"key2020121500495150100_ref039"},{"key":"key2020121500495150100_ref040","first-page":"204","article-title":"Ontology based opinion mining for movie reviews","volume-title":"Knowledge Science, Engineering and Management","year":"2009"},{"issue":"1","key":"key2020121500495150100_ref041","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/T-AFFC.2011.2","article-title":"Aspect-based opinion polling from customer reviews","volume":"2","year":"2011","journal-title":"IEEE Transactions on Affective Computing"},{"year":"2006","first-page":"43","article-title":"Movie review mining and summarization","key":"key2020121500495150100_ref042"}],"container-title":["The Electronic Library"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/EL-11-2014-0197","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/EL-11-2014-0197\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/EL-11-2014-0197\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T01:08:19Z","timestamp":1753405699000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/el\/article\/34\/3\/435-456\/96034"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,6]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2016,6,6]]}},"alternative-id":["10.1108\/EL-11-2014-0197"],"URL":"https:\/\/doi.org\/10.1108\/el-11-2014-0197","relation":{},"ISSN":["0264-0473"],"issn-type":[{"type":"print","value":"0264-0473"}],"subject":[],"published":{"date-parts":[[2016,6,6]]}}}