Domain identification for intention posts on online social media

Title:	Domain identification for intention posts on online social media
Authors:	Luong, T.-L. Truong, Q.-T. Dang, H.-T. Phan, X.-H.
Keywords:	Domain classification;Intention mining;Social media text understanding;Text classification;User intent identification
Issue Date:	2016
Publisher:	Association for Computing Machinery
Citation:	Scopus
Abstract:	Today, more and more Internet users are willing to share their feeling, activities, and even their intention about what they plan to do on online social media. We can easily see posts like "I plan to buy an apartment this year", or "We are looking for a tour for 3 people to Nha Trang" on online forums or social networks. Recognizing those user intents on online social media is really useful for targeted advertising. However fully understanding user intents is a complicated and challenging process which includes three major stages: user intent filtering, intent domain identification, and intent parsing and extraction. In this paper, we propose the use of machine learning to classify intent{holding posts into one of several categories/domains. The proposed method has been evaluated on a medium{sized collections of posts in Vietnamese, and the empirical evaluation has shown promising results with an average accuracy of 88%.
Description:	ACM International Conference Proceeding Series Volume 08-09-December-2016, 8 December 2016, Pages 52-57
URI:	http://repository.vnu.edu.vn/handle/VNU_123/32775
ISBN:	978-145034815-7
Appears in Collections:	Bài báo của ĐHQGHN trong Scopus