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

Dilawar, Noman; Majeed, Hammad; Beg, Mirza Omer; Ejaz, Naveed; Muhammad, Khan; Mehmood, Irfan; Nam, Yunyoung

doi:10.3390/app8091589

Cited by 25 publications

(16 citation statements)

References 22 publications

(24 reference statements)

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“…In smart cities, whose architectural technologies will be involved in collecting, analyzing, and using data for their operations, new privacy and security challenges will arise [75][76][77][78]. Transparency in the design, implementation, and operations of smart city products can ensure establishing private and secure personal spaces for the citizens by enabling their validation and accountability.…”

Section: Discussionmentioning

confidence: 99%

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On Transparency and Accountability of Smart Assistants in Smart Cities

et al. 2019

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Smart Assistants have rapidly emerged in smartphones, vehicles, and many smart home devices. Establishing comfortable personal spaces in smart cities requires that these smart assistants are transparent in design and implementation—a fundamental trait required for their validation and accountability. In this article, we take the case of Google Assistant (GA), a state-of-the-art smart assistant, and perform its diagnostic analysis from the transparency and accountability perspectives. We compare our discoveries from the analysis of GA with those of four leading smart assistants. We use two online user studies (N = 100 and N = 210) conducted with students from four universities in three countries (China, Italy, and Pakistan) to learn whether risk communication in GA is transparent to its potential users and how it affects them. Our research discovered that GA has unusual permission requirements and sensitive Application Programming Interface (API) usage, and its privacy requirements are not transparent to smartphone users. The findings suggest that this lack of transparency makes the risk assessment and accountability of GA difficult posing risks to establishing private and secure personal spaces in a smart city. Following the separation of concerns principle, we suggest that autonomous bodies should develop standards for the design and development of smart city products and services.

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Section: Discussionmentioning

confidence: 99%

“…We insist that these standards should be developed by autonomous bodies and not by the entities whose interests lie in the related business. Moreover, as suggested by Zouave and Marquenie [36] and Dilawar et al [77], all stakeholders of the smart city products should be involved while developing these standards.…”

Section: Discussionmentioning

confidence: 99%

On Transparency and Accountability of Smart Assistants in Smart Cities

et al. 2019

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“…Statistical characteristics of the full dataset used in our sentiment analysis tasks. The upper and lower value in columns (4)- (6) represent the number of all tokens and the number of distinct tokens, respectively.…”

Section: The Datasetmentioning

confidence: 99%

“…Methods of automatic sentiment analysis are commonly chosen for this purpose. Sentiment analysis has been used for analyzing sentiments of tweets on health topics [1], evaluate teachers ability and predict student performance [2], identify contextual polarity of drugs, movie, and restaurant reviews [3][4][5], examine public opinion on products and services and societal issues [6], classify e-learners and their topics of interest in their social networks interactions [7], perform opinion mining in tweets [8,9], analyze sentiment orientation of microblogs [10], and forecast stock prices from sentiments of news signals in the financial markets [11].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

2019

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We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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“…People's participation occurs in a variety of ways [4,5]. Considering people's perceptions and their cognition of the environment that they live in can play a key role in successful urban planning based upon people's actual needs [6][7][8]. The feelings and expectations of people regarding space and their living space, with regard to their cultural and social characteristics, have always held researchers' interests [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer

et al. 2019

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Considering citizens’ perceptions of their living environment is very helpful in making the right decisions for city planners who intend to build a sustainable society. Mental map analyses are widely used in understanding the level of perception of individuals regarding the surrounding environment. The present study introduces Aram Mental Map Analyzer (AMMA), an open-source program, which allows researchers to use special features and new analytical methods to receive outputs in numerical data and analytical maps with greater accuracy and speed. AMMA performance is contingent upon two principles of accuracy and complexity, the accuracy of the program is measured by Accuracy Placed Landmarks (APL) and General Orientation (GO), which respectively analyses the landmark placement accuracy and the main route mapping accuracy. Also, the complexity section is examined through two analyses Cell Percentage (CP) and General Structure (GS), which calculates the complexity of citizens’ perception of space based on the criteria derived from previous studies. AMMA examines all the dimensions and features of the graphic maps and its outputs have a wide range of valid and differentiated information, which is tailored to the research and information subject matter that is required.

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

Cited by 25 publications

References 22 publications

On Transparency and Accountability of Smart Assistants in Smart Cities

On Transparency and Accountability of Smart Assistants in Smart Cities

Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer

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