Volume 7, Issue 5 (May 2020), Pages: 20-26

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 Original Research Paper

 Title: Semi-supervised method for sensitivity based documents’ classification for online service providers

 Author(s): Sharaf J. Malebary *, Shakeel Ahmad

 Affiliation(s):

 Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4339-3791

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.05.004

 Abstract:

In today’s digital era, many services providing companies exist on the web whereas service is the logical product of a company, which can be utilized through the Internet. Different service providers provide these services i.e., Online counselling service, online doctor consultation, cloud service provider, web hosting service, etc. to their customers. When customers face some problems, they may text to their providers. One solution is that providers can solve these issues based on the First-Come-First-Serve formula. But there should be an option to detect sensitive issue which may need to be solved first. How can this sensitivity be determined? Already there is a lot of researched work based on text to determine the polarity as positive and negative. Besides this classification, there are also some other classification methods investigated, such as aspect, not aspect, subjective, objective, spam, not spam, etc. regarding text sensitivity, whether it is sensitive or not? This classification is not yet considered for service providers. This paper presents a strategy for sensitivity based classification using Latent Semantic Indexing (LSI). The purpose of LSI is to rank documents concerning a given query. However, in this study, a mechanism was provided to generate query automatically based on sensitive general words with the words from all documents. This is a semi-supervised approach because 4782 sensitive words have been labeled from various sources and used based on an unsupervised approach to detect the sensitivity of the document. The sorted lists of documents based on the LSI scores generated by the sensitive-query were checked manually and were proved to be highly satisfactory. The topmost document in this list was the most sensitive, and the last document in the list was least sensitive. 

 © 2020 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: Service, Sentiment analysis, Supervised learning method, Unsupervised learning method, Latent semantic indexing

 Article History: Received 4 November 2019, Received in revised form 5 February 2020, Accepted 7 February 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

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

 Citation:

 Malebary SJ and Ahmad S (2020). Semi-supervised method for sensitivity based documents’ classification for online service providers. International Journal of Advanced and Applied Sciences, 7(5): 20-26

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 Figures

 Fig. 1 Fig. 2 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 

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