Applying deep neural networks for user intention identification
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METHODOLOGIES AND APPLICATION
Applying deep neural networks for user intention identification Asad Khattak1 • Anam Habib2 • Muhammad Zubair Asghar2 Ammara Habib2
•
Fazli Subhan3 • Imran Razzak4
•
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods. Keywords Intention identification Intention mining Deep learning CNN LSTM Product reviews Social media services
1 Introduction Communicated by V. Loia.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00500-020-05290-z) contains supplementary material, which is available to authorized users. & Muhammad Zubair Asghar [email protected] Asad Khattak [email protected] Anam Habib [email protected] Fazli Subhan [email protected] Imran Razzak [email protected]
Social media revolution has brought the people closer for sharing their ideas, experiences and opinions on Twitter, Facebook and other social media platforms. Such platforms are also widely being used by business organizations to collect and analyze customer response about their 1
College of Technological Innovation, Zayed University, 144534 Abu Dhabi Campus, UAE
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Institute of Computing and Information Technology, Gomal University, D.I.Khan (KP), Pakistan
3
Department Of Computer Science, Faculty of Engineering and Computer Science, NUML University, Islamabad, Pakistan
4
School of Information Technology, Deakin University, Geelong, Australia
Ammara Habib [email protected]
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A. Khattak et al.
manufactured products. The classical opinion mining techniques mainly focus
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