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Learning to Filter User Explicit Intents in Online Vietnamese Social Media Texts

Thai Le Luong and Thi Hanh Trang and Quoc Tuan Truong and Thi Minh Ngoc Truong and Thi Thu Phi and Xuan Hieu Phan (2016) Learning to Filter User Explicit Intents in Online Vietnamese Social Media Texts. In: SW4PHD: the 2016 Scientific Workshop for PhD Students, 26 March 2016, Hanoi.

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Abstract

Today, Internet users are much more willing to express them- selves on online social media channels. They commonly share their daily activities, their thoughts or feelings, and even their intention (e.g., buy a camera, rent an apartment, borrow a loan, etc.) about what they plan to do on blogs, forums, and especially online social networks. Understand- ing intents of online users, therefore, has become a crucial need for many enterprises operating in different business areas like production, banking, retail, e–commerce, and online advertising. In this paper, we will present a machine learning approach to analyze users’ posts and comments on online social media to filter posts or comments containing user plans or intents. Fully understanding user intent in social media texts is a compli- cated process including three major stages: user intent filtering, intent domain identification, and intent parsing and extraction. In the scope of this study, we will propose a solution to the first one, that is, building a binary classification model to determine whether a post or comment carries an intent or not. We carefully conducted an empirical evaluation for our model on a medium–sized collection of posts in Vietnamese and achieved promising results with an average accuracy of more than 90%. Keywords: Intention mining, user intent identification, social media text understanding, content filtering, text classification

Item Type:Conference or Workshop Item (Poster)
Subjects:Information Technology (IT)
Divisions:Faculty of Information Technology (FIT)
ID Code:1527
Deposited By: Dr Ngoc Thang Bui
Deposited On:23 May 2016 05:59
Last Modified:23 May 2016 05:59

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