Tourism Mobile App With Aspect-Based Sentiment Classification Framework for Tourist Reviews

Afzaal, Muhammad; Usman, Muhammad; Fong, A.C.M.

doi:10.1109/tce.2019.2908944

Cited by 73 publications

(44 citation statements)

References 28 publications

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“…According to step 1, he or she takes the users' reviews about these four mobile phones from Tmall.com, JD.com, and Suning.com to extract the attributes and perform emotional computing. Then the attribute and opinion words of each phone are obtained by deep learning and clustering algorithm, there are final six attributes, including Appearance C ( ) 1 , System C ( ) 2 , Fingerprint Unlocking C ( ) 3 , Service C ( ) 4 , Photograph C ( ) 5 , and Price C ( ) 6 , to form an attribute set C C C C C C C = { , , , , , } 1 2 3 4 5 6 , the polarity and the number of opinion sentiments can be calculated, as shown in Tables 4 to 6. According to step 2, calculate the sentiment scores of each product on different platforms, as shown in Tables 7 to 9.…”

Section: Procedures Of Decision-makingmentioning

confidence: 99%

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A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

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In the process of online shopping, consumers usually compare the review information of the same product in different e‐commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep‐learning–based opinion mining and q‐rung orthopair fuzzy interaction weighted Heronian mean (q‐ROFIWHM) operators. First, in the proposed method, the deep‐learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute‐opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross‐decision matrices, which are represented by the q‐rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q‐ROFIWHM operators are proposed to aggregate this cross‐decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.

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Section: Procedures Of Decision-makingmentioning

confidence: 99%

“…In addition, the sentiment orientations of online reviews on all platforms are multi‐aspects, 4‐6 such as positive, negative, and neutral sentiments. The basis for sentiment analysis in product reviews lies in the accurate extraction of attribute‐opinion word pairs.…”

Section: Introductionmentioning

confidence: 99%

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

View full text Add to dashboard Cite

show abstract

“…To achieve the diversity objective for this experiment, we tested the performance on three smartphones of different capacity, in terms of RAM (Random Access Memory) battery power, CPU (Central Processing Unit), and hard disk drive (HDD) [48]. We deliberately chose a legacy, 1 latest and one somewhat older mobile phone to add variety and the year of manufacturing.…”

Section: Performance Test Of the Appmentioning

confidence: 99%

A mobile app to make biometrics usage more secure

Singh¹,

Singh²

2020

Preprint

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With high paced growth in biometrics, its easy availability to capture various biometric features, it is <a>emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. </a>Organizations encourage their members to use biometrics, but <a>they are hesitant to use due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. </a>We propose a solution of an extra layer of security, via a low-cost mobile app framework, “Bio-Guard,” to use biometrics, more securely. We tested the app for its design, functions, usability, and it got a score of more than 71% on the usability scale and a reasonably low equal error rate (EER) of 6%. The survey to evaluate the usefulness of the app showed a favorable response of 80%. The results show a good potential of the app to make access to biometric data more secure. The app may enhance users’ confidence-level in encouraging higher participation of users in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many medium and small segment organizations to decrease their information security risk.

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“…I. Linear Search [1] Linear Search Algorithm is used here to find the highest visited place and also to find the place which has been visited the most. [1] A simple approach is to do linear search, i.e.…”

Section: B) Algorithmmentioning

confidence: 99%

“…Linear Search [1] Linear Search Algorithm is used here to find the highest visited place and also to find the place which has been visited the most. [1] A simple approach is to do linear search, i.e. [1]  Start from the leftmost element of arr[] and one by one compare x with each element of arr[] [1]  If x matches with an element, return the index.…”

Section: B) Algorithmmentioning

confidence: 99%

Intelligent Tourist Guide System using Web Development

Islam,

Priya,

Sharma

2019

IJEAT

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There is a huge development in the Information Technology or IT sector recently. Also, there is a massive use of Geographic Information System or in short, we say as GIS nowadays. Tourism Industry is getting more and more advantage specially from GIS. GIS and Tourism Industry are bonding together in a smooth and progressive manner. In order to connect GIS and Tourism, that means in order to establish a bonding between them network is required or we can say connectivity is required. Network allows nodes to share resources in digital telecommunications. So, GIS and Tourism are interconnected with network. Network form infrastructure of modern society. When tourists visit a new place, they might not know about the place in a detailed manner or it might be just a new place for them. Even services like Google which uses the Navigation system and uses the GIS system might not have all details of a place of very low tourist interests. But here we propose Intelligent Tourist Guide System using Web Development which will help tourists travelling to different parts of the world and also local people can advertise their place in order to make it a Tourist Spot and make their area famous.

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Tourism Mobile App With Aspect-Based Sentiment Classification Framework for Tourist Reviews

Cited by 73 publications

References 28 publications

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

A mobile app to make biometrics usage more secure

Intelligent Tourist Guide System using Web Development

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