{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:54:33Z","timestamp":1767084873926,"version":"3.41.2"},"reference-count":61,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2019,12,23]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to propose a methodology to construct a stock market sentiment lexicon by incorporating domain-specific knowledge extracted from diverse Chinese media outlets.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This paper presents a novel method to automatically generate financial lexicons using a unique data set that comprises news articles, analyst reports and social media. Specifically, a novel method based on keyword extraction is used to build a high-quality seed lexicon and an ensemble mechanism is developed to integrate the knowledge derived from distinct language sources. Meanwhile, two different methods, Pointwise Mutual Information and Word2vec, are applied to capture word associations. Finally, an evaluation procedure is performed to validate the effectiveness of the method compared with four traditional lexicons.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The experimental results from the three real-world testing data sets show that the ensemble lexicons can significantly improve sentiment classification performance compared with the four baseline lexicons, suggesting the usefulness of leveraging knowledge derived from diverse media in domain-specific lexicon generation and corresponding sentiment analysis tasks.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This work appears to be the first to construct financial sentiment lexicons from over 2m posts and headlines collected from more than one language source. Furthermore, the authors believe that the data set established in this study is one of the largest corpora used for Chinese stock market lexicon acquisition. This work is valuable to extract collective sentiment from multiple media sources and provide decision-making support for stock market participants.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/imds-04-2019-0254","type":"journal-article","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T09:28:42Z","timestamp":1577957322000},"page":"508-525","source":"Crossref","is-referenced-by-count":5,"title":["Inducing stock market lexicons from disparate Chinese texts"],"prefix":"10.1108","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6611-759X","authenticated-orcid":false,"given":"Futao","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Zhong","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Luan","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"2019","key":"key2020032509574058300_ref001","first-page":"118","article-title":"Stock market response to information diffusion through internet sources: a literature review","volume":"45","year":"2019","journal-title":"International Journal of Information Management"},{"first-page":"697","article-title":"Arasenti: large-scale twitter-specific Arabic sentiment lexicons","year":"2016","key":"key2020032509574058300_ref002"},{"article-title":"Building a sentiment summarizer for local service reviews","volume-title":"Proceedings of the WWW2008 Workshop: NLP in the Information Explosion Era (NLPIX 2008), Beijing","year":"2008","key":"key2020032509574058300_ref003"},{"issue":"4","key":"key2020032509574058300_ref004","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.2307\/41703504","article-title":"Business intelligence in blogs: understanding consumer interactions and communities","volume":"36","year":"2012","journal-title":"MIS Quarterly"},{"issue":"2014","key":"key2020032509574058300_ref005","first-page":"61","article-title":"Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews","volume":"71","year":"2014","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"key2020032509574058300_ref006","first-page":"22","article-title":"Word association norms, mutual information, and lexicography","volume":"16","year":"1990","journal-title":"Computational Linguistics"},{"issue":"1-2","key":"key2020032509574058300_ref007","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/15377857.2011.540224","article-title":"From networked nominee to networked nation: examining the impact of Web 2.0 and social media on political participation and civic engagement in the 2008 Obama campaign","volume":"10","year":"2011","journal-title":"Journal of Political Marketing"},{"issue":"2017","key":"key2020032509574058300_ref008","first-page":"65","article-title":"Adapting sentiment lexicons to domain-specific social media texts","volume":"94","year":"2017","journal-title":"Decision Support Systems"},{"issue":"1","key":"key2020032509574058300_ref009","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1108\/JPBM-06-2014-0635","article-title":"Consumer engagement in online brand communities: a social media perspective","volume":"24","year":"2015","journal-title":"Journal of Product & Brand Management"},{"issue":"1","key":"key2020032509574058300_ref010","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1177\/0267323116682802","article-title":"Twitter as arena for the authentic outsider: exploring the social media campaigns of Trump and Clinton in the 2016 US presidential election","volume":"32","year":"2017","journal-title":"European Journal of Communication"},{"issue":"6","key":"key2020032509574058300_ref011","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/2602574","article-title":"The power of social media analytics","volume":"57","year":"2014","journal-title":"Communications of the ACM"},{"issue":"4","key":"key2020032509574058300_ref012","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1145\/2436256.2436274","article-title":"Techniques and applications for sentiment analysis","volume":"56","year":"2013","journal-title":"Communications of the ACM"},{"issue":"2014","key":"key2020032509574058300_ref013","first-page":"212","article-title":"Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news","volume":"57","year":"2014","journal-title":"Decision Support Systems"},{"key":"key2020032509574058300_ref014","unstructured":"Goldberg, Y. and Levy, O. 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