TY  - JOUR
AB  - Purpose– 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. Design/methodology/approach– 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. Findings– 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. Research limitations/implications– 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. Originality/value– 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.
VL  - 66
IS  - 5
SN  - 2050-3806
DO  - 10.1108/AJIM-12-2013-0141
UR  - https://doi.org/10.1108/AJIM-12-2013-0141
AU  - Thanh Nguyen Tung
AU  - Thanh Quan Tho
AU  - Thi Phan Tuoi
ED  - Fran Alexander and Dr Ulrike Spree
PY  - 2014
Y1  - 2014/01/01
TI  - Sentiment search: an emerging trend on social media monitoring systems
T2  - Aslib Journal of Information Management
PB  - Emerald Group Publishing Limited
SP  - 553
EP  - 580
Y2  - 2024/04/26
ER  -