Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2016

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%.

Keywords

Domain classification, Intention mining, Social media text understanding, Text classification, User intent identification

Discipline

Computer Sciences | Numerical Analysis and Scientific Computing | Social Media

Publication

SoICT '16: Proceedings of the Seventh Symposium on Information and Communication Technology: Ho Chi Minh, Vietnam, December 8-9, 2016

First Page

52

Last Page

57

ISBN

9781450348157

Identifier

10.1145/3011077.3011134

Publisher

ACM

City or Country

New York

Additional URL

http://doi.org/10.1145/3011077.3011134

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