The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion.
The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains.
The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques.
The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks.
The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.
This work is supported by the research project B0212-20-02TD funded by the Vietnam National University – Ho Chi Minh City. The authors are also grateful to YouNet Media for supporting real data sets for the experiments.
Thanh Nguyen, T., Thanh Quan, T. and Thi Phan, T. (2014), "Sentiment search: an emerging trend on social media monitoring systems", Aslib Journal of Information Management, Vol. 66 No. 5, pp. 553-580. https://doi.org/10.1108/AJIM-12-2013-0141Download as .RIS
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