Microblog sentiment analysis with weak dependency connections
By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- Fresh data on Engineering - Knowledge Engineering are presented in a new report. According to news originating from Heilongjiang, People’s Republic of China, by VerticalNews correspondents, research stated, “With the rise of microblogging services like Twitter and Sina Weibo, users are able to post their real-time mood and opinions conveniently and swiftly. At the same time, the ubiquitous social media results in abundant social relations such as following and follower relations.”
Funders for this research include National Natural Science Foundation of China, Youth Science Foundation of Heilongjiang, Heilongjiang postdoctoral Fund.
Our news journalists obtained a quote from the research from Harbin Engineering University, “Social relations create a new source for microblog sentiment analysis, which attracts a great amount of attention in recent years. There are two theories that support the use of social relations for sentiment analysis - sentiment consistency and emotional contagion. However, most existing microblog sentiment analysis methods only employ direct connections which cannot fully use the heterogeneous connections in social media. As online social networks consist of communities and nodes in the same community which form weak dependency connections usually share similarities, we investigate how to exploit weak dependency connections as an aspect of social contexts for microblog sentiment analysis in this paper. In particular, we employ community detection methods to capture weak dependency connections and propose a new model for microblog sentiment analysis which incorporates weak dependency connections, sentiment consistency, and emotional contagion together with text information.”
According to the news editors, the research concluded: “Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.”
For more information on this research see: Microblog sentiment analysis with weak dependency connections. Knowledge-Based Systems , 2018;142():170-180. Knowledge-Based Systems can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Knowledge-Based Systems - http://www.journals.elsevier.com/knowledge-based-systems/)
The news correspondents report that additional information may be obtained from J. Yang, Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Heilongjiang, People’s Republic of China. Additional authors for this research include X.M. Zou, J.P. Zhang and H.Y. Han.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.knosys.2017.11.035. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-02-26), New Findings in Knowledge Engineering Described from Harbin Engineering University (Microblog sentiment analysis with weak dependency connections), Journal of Robotics & Machine Learning, 106, ISSN: 1944-186X, BUTTER® ID: 015216385
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