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Suggestion Mining from Opinionated Text

Publication Type: 
Book Chapter
Abstract: 
Products and services are heavily discussed on social media, which are conventionally used by brand owners, as well as consumers to acquire consumer opinions. State of the art opinion mining systems provide the summaries of positive and negative sentiments towards a service/product and its various aspects. On a closer look, it is observed that these opinions also contain suggestions, tips, and advice, which are often explicitly sought by both brand owners and consumers. This chapter presents a comprehensive overview of the task of mining suggestions from the opinionated text on social media. Various aspects of the task are discussed, which includes an analysis of suggestions appearing in reviews, relation between sentiments and suggestions, relevant datasets, and existing methodologies. The problem has been identi ed only recently as a viable task, and there is a limited availability of existing approaches and datasets. Keywords: Suggestion Mining, Advice Mining, Wish Mining, Moods and Modality, Reviews
Detail of Publication: 
Book Title: The Handbook of Sentiment Analysis in Social Networks Editors: Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu Publisher: Elsevier
Publication Date: 
15/08/2016
Research Group: 
Institution: 
NUIG
Open access repository: 
No