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Sentiment analysis as one of the most important of these analyses gains increasing interests. However, the research in this field is still facing challenges. The mainstream of the sentiment analysis research on social media websites and microblogs just exploits the textual content of the posts. This makes the analysis hard because microblog posts are short and noisy. However, they have lots of contexts which can be exploited for sentiment analysis. In order to use the context as an auxiliary source, some recent papers use reply\/retweet to model the context of the target post. We claim that multiple sequential contexts can be used jointly in a unified model. In this article, we propose a context-aware multi-thread hierarchical long short-term memory (MHLSTM) that jointly models different kinds of contexts, such as tweep, hashtag and reply besides the content of the target post. Experimental evaluations on a real-world Twitter data set demonstrate that our proposed model can outperform some strong baseline models by 28.39% in terms of relative error reduction.<\/jats:p>","DOI":"10.1177\/0165551521990617","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T17:56:55Z","timestamp":1613843815000},"page":"133-144","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-thread hierarchical deep model for context-aware sentiment analysis"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6826-4692","authenticated-orcid":false,"given":"Abdalsamad","family":"Keramatfar","sequence":"first","affiliation":[{"name":"Computer Engineering and Information Technology Department, University of Qom, Iran"}]},{"given":"Hossein","family":"Amirkhani","sequence":"additional","affiliation":[{"name":"Computer Engineering and Information Technology Department, University of Qom, Iran"}]},{"given":"Amir","family":"Jalaly Bidgoly","sequence":"additional","affiliation":[{"name":"Computer Engineering and Information Technology Department, University of Qom, Iran"}]}],"member":"179","published-online":{"date-parts":[[2021,2,15]]},"reference":[{"key":"bibr1-0165551521990617","first-page":"30","volume-title":"Proceedings of the workshop on language in social media (LSM 2011)","author":"Agarwal A"},{"key":"bibr2-0165551521990617","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2019.2904691"},{"key":"bibr3-0165551521990617","doi-asserted-by":"publisher","DOI":"10.1177\/0165551520917099"},{"key":"bibr4-0165551521990617","doi-asserted-by":"publisher","DOI":"10.1177\/0165551518761013"},{"key":"bibr5-0165551521990617","volume-title":"Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers)","author":"Poria S"},{"key":"bibr6-0165551521990617","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0191163"},{"key":"bibr7-0165551521990617","doi-asserted-by":"publisher","DOI":"10.1145\/2938640"},{"key":"bibr8-0165551521990617","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.05.003"},{"key":"bibr9-0165551521990617","unstructured":"Go A, Bhayani R, Huang L. 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