Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets

By Jinpeng Wang, Gao Cong, Wayne Xin Zhao, Xiaoming Li. 2015

85 citations recorded by the source corpus.

In this paper, we propose to study the problem of identifying and classifying tweets into intent categories. For example, a tweet “I wanna buy a new car” indicates the user’s intent for buying a car. Identifying such intent tweets will have great commercial value among others. In particular, it is important that we can distinguish different types of intent tweets. We propose to classify intent tweets into six categories, namely Food & Drink, Travel, Career & Education, Goods & Services, Event and Activities and Trifle. We propose a semisupervised learning approach to categorizing intent tweets into the six categories.We construct a test collection by using a bootstrap method. Our experimental results show that our approach is effective in inferring intent categories for tweets.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

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Citations in the corpus

Towards Open Intent Discovery for Conversational Text (2019, methodology): “Supervised and semi-supervised learning models based on linguistic and sentiment features have been used to model racial intent on Tumblr [1], and purchase intent in social posts [14, 47] and in discussion forums [5].”
A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “POS tags indicate the role of the words in a sentence (Wang et al. 2015a).”
Characterizing Linguistic Attributes for Automatic Classification of Intent Based Racist/Radicalized Posts on Tumblr Micro-Blogging Website (2017, result): “In contrast to the existing work, our paper makes the following novel contributions: 1) To the best of our knowledge, the study presented in this paper is the ﬁrst work on racist and radicalization detection based on the intent of narrative unlike previous keyword spotting methods.”