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Appl. Sci. 2018, 8(9), 1589; https://doi.org/10.3390/app8091589

Understanding Citizen Issues through Reviews: A Step towards Data Informed Planning in Smart Cities

1
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad 46000, Pakistan
2
Department of Computing and Technology, Iqra University, Islamabad 46000, Pakistan
3
Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143747, Korea
4
Department of Software, Sejong University, Seoul 143747, Korea
5
Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
*
Authors to whom correspondence should be addressed.
Received: 27 July 2018 / Revised: 24 August 2018 / Accepted: 28 August 2018 / Published: 7 September 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Governments these days are demanding better Smart City technologies in order to connect with citizens and understand their demands. For such governments, much needed information exists on social media where members belonging to diverse groups share different interests, post statuses, review and comment on various topics. Aspect extraction from this data can provide a thorough understanding of citizens’ behaviors and choices. Also, categorization of these aspects can better summarize societal concerns regarding political, economic, religious and social issues. Aspect category detection (ACD) from people reviews is one of the major tasks of aspect-based sentiment analysis (ABSA). The success of ABSA is mainly defined by the inexpensive and accurate machine-processable representation of the raw input sentences. Previous approaches rely on cumbersome feature extraction procedures from sentences, which adds its own complexity and inaccuracy in performing ACD tasks. In this paper, we propose an inexpensive and simple method to obtain the most suitable representation of a sentence-vector through different algebraic combinations of a sentence’s word vectors, which will act as an input to any machine learning classifier. We have tested our technique on the restaurant review data provided in SemEval-2015 and SemEval-2016. SemEval is a series of global challenges to evaluate the effectiveness of disambiguation of word sense. Our results showed the highest F1-scores of 76.40% in SemEval-2016 Task 5, and 94.99% in SemEval-2015 Task 12. View Full-Text
Keywords: smart cities; supervised learning; aspect category detection; aspect-based sentiment analysis smart cities; supervised learning; aspect category detection; aspect-based sentiment analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dilawar, N.; Majeed, H.; Beg, M.O.; Ejaz, N.; Muhammad, K.; Mehmood, I.; Nam, Y. Understanding Citizen Issues through Reviews: A Step towards Data Informed Planning in Smart Cities. Appl. Sci. 2018, 8, 1589.

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