International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 8 (August 2019), Pages: 23-31

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 Original Research Paper

 Title: Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

 Author(s): Sheikh Muhammad Saqib 1, *, Fazal Masud Kundi 1, Shakeel Ahmad 2, Tariq Naeem 1

 Affiliation(s):

 1Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan
 2Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdul Aziz University (KAU), Jeddah, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-4647-1698

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.08.004

 Abstract:

Posted reviews on the relevant webpages about a product not only motivate the company to enhance quality but also it helps users to decide in favor of (or against) purchasing the product. These reviews are classified by different researchers through subjectivity based, entity based, or aspect based to find the polarity using the supervised or unsupervised technique. However, classification based on interrogatives and non-interrogatives is not handled yet. Datasets of interrogatives are analyzed as identifying Answer Seeking questions from Arabic tweets, question conveying and not conveying Information, Rhetorical Questions while here classifying the sentences into interrogatives and non-interrogatives is the preliminary step, which is a core contribution of proposed work. If detected questions are answered and moreover real time, it could not only motivate a user positively to buy the product but also users feel full duplex communication. In this work, we formulated this problem proposing linguistic and heuristic rules that automatically senses the interrogative and answer promptly based on the aforementioned aspect. If there is no aspect in an asked question, then LSI (Latent Semantic Indexing) generate answer using classified non-interrogatives. LSI is an efficient information retrieval algorithm, which finds the closest document to a given query. Experimental results using two publically available datasets show a precision of 95% and 96% which has 10% increased performance than alternatives machine learning methods Meta Filtered Classifier and Naive Bayes. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Sentiment classification, Preprocessing, Text mining, Sentiment orientation

 Article History: Received 12 March 2019, Received in revised form 3 June 2019, Accepted 4 June 2019

 Acknowledgement:

No Acknowledgement.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Saqib SM, Kundi FM, and Ahmad S et al. (2019). Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer. International Journal of Advanced and Applied Sciences, 6(8): 23-31

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10

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