Identifying Purchase Intent from Social Posts
By Vineet Gupta, Devesh Varshney, Harsh Jhamtani, Deepam Kedia, Shweta Karwa. 2014
53 citations recorded by the source corpus.
In present times, social forums such as Quora and Yahoo! Answers constitute powerful media through which people discuss on a variety of topics and express their intentions and thoughts. Here they often reveal their potential intent to purchase - 'Purchase Intent' (PI). A purchase intent is defined as a text expression showing a desire to purchase a product or a service in future. Extracting posts having PI from a user's social posts gives huge opportunities towards web personalization, targeted marketing and improving community observing systems. In this paper, we explore the novel problem of detecting PIs from social posts and classifying them. We find that using linguistic features along with statistical features of PI expressions achieves a significant improvement in PI classification over 'bag-of-words' based features used in many present day social-media classification tasks. Our approach takes into consideration the specifics of social posts like limited contextual information, incorrect grammar, language ambiguities, etc. by extracting features at two different levels of text granularity - word and phrase based features and grammatical dependency based features. Apart from these, the patterns observed in PI posts help us to identify some specific features.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Srinivasan Parthasarathy
- Kristina Lerman
- Farshad Kooti
- Nemanja Djuric
- Nikhita Vedula
- Nedim Lipka
- Mihajlo Grbovic
- Luca Maria Aiello
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].”
- Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior (2015, background): “A better understanding of consumer behavior can help improve marketing efforts and make online shopping more pleasant and efficient.”