Abstract
Consumer reviews provide a wealth of information about products and services that, if properly identified and extracted, could be of immense value to businesses. While classification of reviews according to sentiment polarity has been extensively studied in previous work, more focused types of review analysis are needed to assist companies in making business decisions. In this work, we introduce a novel text classification problem of separating post-purchase from pre-purchase review fragments that can facilitate identification of immediate actionable insights based on the feedback from the customers, who actually purchased and own a product. To address this problem, we propose the features, which are based on the dictionaries and part-of-speech (POS) tags. Experimental results on the publicly available gold standard indicate that the proposed features allow to achieve nearly 75 % accuracy for this problem and improve the performance of classifiers relative to using only lexical features.
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Notes
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Gold standard and dictionaries are available at http://github.com/teanalab/prepost.
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Acknowledgements
This work was supported in part by an unrestricted gift from Ford Motor Company.
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Hasan, M., Kotov, A., Mohan, A., Lu, S., Stieg, P.M. (2016). Feedback or Research: Separating Pre-purchase from Post-purchase Consumer Reviews. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_53
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DOI: https://doi.org/10.1007/978-3-319-30671-1_53
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