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Mining purchase intent in Twitter

Haque, Rejwanul ORCID: 0000-0003-1680-0099, Ramadurai, Arvind, Hasanuzzaman, Mohammed ORCID: 0000-0003-1838-0091 and Way, Andy ORCID: 0000-0001-5736-5930 (2019) Mining purchase intent in Twitter. Computacion y Sistemas, 23 (3). pp. 871-881. ISSN 2007-9737

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Abstract

Most social media platforms allow users to freely express their beliefs, opinions, thoughts, and intents. Twitter is one of the most popular social media platforms where users’ post their intent to purchase. A purchase intent can be defined as measurement of the probability that a consumer will purchase a product or service in future. Identification of purchase intent in Twitter sphere is of utmost interest as it is one of the most long-standing and widely used measures in marketing research. In this paper, we present a supervised learning strategy to identify users’ purchase intent from the language they use in Twitter. Recurrent Neural Networks (RNNs), in particular with Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for text classification. They effectively encode sequences with varying length and capture long range dependencies. We present the first study to apply LSTM for purchase intent identification task. We train the LSTM network on semi-automatically created dataset. Our model achieves competent classification accuracy (F1 = 83%) over a gold-standard dataset. Further, we demonstrate the efficacy of the LSTM network by comparing its performance with different classical classification algorithms taking this purchase intent identification task into account.

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Social media; purchase intent; mining; user generated content
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:Instituto Politécnico Nacional
Official URL:http://dx.doi.org/10.13053/cys-23-3-3254
Copyright Information:© 2019 Instituto Politécnico Nacional. (Open)
Funders:Science Foundation Ireland (SFI) (Grant No. 13/RC/2106), European Regional Development Fund, European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie Grant Agreement No. 713567, Science Foundation Ireland (SFI) under Grant Number 13/RC/2077.
ID Code:24610
Deposited On:16 Jun 2020 11:25 by Vidatum Academic . Last Modified 04 Jan 2021 17:05

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