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Conference Paper: Investment recommendation using investor opinions in social media

TitleInvestment recommendation using investor opinions in social media
Authors
Issue Date2016
PublisherACM Press.
Citation
The 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), Pisa, Italy, 17-21 July 2016. In Conference Proceedings, 2016, p. 881-884 How to Cite?
AbstractInvestor social media, such as StockTwist, are gaining increasing popularity. These sites allow users to post their investing opinions and suggestions in the form of microblogs. Given the growth of the posted data, a significant and challenging research problem is how to utilize the personal wisdom and different viewpoints in these opinions to help investment. Previous work aggregates sentiments related to stocks and generates buy or hold recommendations for stocks obtaining favorable votes while suggesting sell or short actions for stocks with negative votes. However, considering the fact that there always exist unreasonable or misleading posts, sentiment aggregation should be improved to be robust to noise. In this paper, we improve investment recommendation by modeling and using the quality of each investment opinion. To model the quality of an opinion, we use multiple categories of features generated from the author information, opinion content and the characteristics of stocks to which the opinion refers. Then, we discuss how to perform investment recommendation (including opinion recommendation and portfolio recommendation) with predicted qualities of investor opinions. Experimental results on real datasets demonstrate effectiveness of our work in recommending high-quality opinions and generating profitable investment decisions. © 2016 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/230547
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTu, W-
dc.contributor.authorCheung, DWL-
dc.contributor.authorMamoulis, N-
dc.contributor.authorYang, M-
dc.contributor.authorLu, Z-
dc.date.accessioned2016-08-23T14:17:40Z-
dc.date.available2016-08-23T14:17:40Z-
dc.date.issued2016-
dc.identifier.citationThe 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), Pisa, Italy, 17-21 July 2016. In Conference Proceedings, 2016, p. 881-884-
dc.identifier.isbn978-1-4503-4069-4-
dc.identifier.urihttp://hdl.handle.net/10722/230547-
dc.description.abstractInvestor social media, such as StockTwist, are gaining increasing popularity. These sites allow users to post their investing opinions and suggestions in the form of microblogs. Given the growth of the posted data, a significant and challenging research problem is how to utilize the personal wisdom and different viewpoints in these opinions to help investment. Previous work aggregates sentiments related to stocks and generates buy or hold recommendations for stocks obtaining favorable votes while suggesting sell or short actions for stocks with negative votes. However, considering the fact that there always exist unreasonable or misleading posts, sentiment aggregation should be improved to be robust to noise. In this paper, we improve investment recommendation by modeling and using the quality of each investment opinion. To model the quality of an opinion, we use multiple categories of features generated from the author information, opinion content and the characteristics of stocks to which the opinion refers. Then, we discuss how to perform investment recommendation (including opinion recommendation and portfolio recommendation) with predicted qualities of investor opinions. Experimental results on real datasets demonstrate effectiveness of our work in recommending high-quality opinions and generating profitable investment decisions. © 2016 ACM.-
dc.languageeng-
dc.publisherACM Press.-
dc.relation.ispartofProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR '16-
dc.titleInvestment recommendation using investor opinions in social media-
dc.typeConference_Paper-
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hk-
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.authorityCheung, DWL=rp00101-
dc.identifier.authorityMamoulis, N=rp00155-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1145/2911451.2914699-
dc.identifier.scopuseid_2-s2.0-84980401650-
dc.identifier.hkuros262977-
dc.identifier.spage881-
dc.identifier.epage884-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160919-

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