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Revamp Tourism—Utilization of Affective Reasoning, Artificial Intelligence, and Big Data through Natural Language Processing

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3994

Special Issue Editors

School of Computing, DIT University, Dehradun 248009, India
Interests: artificial intelligence; machine learning; information retrieval; natural language processing; computational technologies; sentiment analysis; depression detection; multimodal sentiment analysis; social media; multi-lingual sentiment analysis; emotion analysis
Special Issues, Collections and Topics in MDPI journals
1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 21st century is an era that goes by the name of social connectedness where individuals are exuberant about sharing their opinion, experience, and thoughts through collaborated social networks. This collaboration has resulted in a collective large amount of data in the fields of commerce, tourism, education, and health and thus created a hike in the growth of the web. The extraction of information from this unstructured dataset is a difficult task and has paved the pathway for the usage of emerging advanced technologies, such as Artificial Intelligence, Big Data, and Natural Language Processing, from the domain of Computer Science and Technology. Tourism is among one of the top rising industries that create the potential for global wealth and employment; hence, we need smart, actionable solutions to draw insights on marketing campaigns and the interest of the visitor. The decisions based on the evidence of analytics of number-driven data can help the players of the tourism industry in identifying the potential sectors in the field of tourism and hospitality.

In this context, this Special Issue focuses on the novel approaches of Big Data, Artificial Intelligence, and Natural Language processing along with Affective Reasoning and Sentiment Analysis. The tourism industry is among the industries that have adopted the emerging technologies at a good pace and are working towards the light of achieving a reduction in time and money. A key motivation for this Special Issue is to explore the adoption of novel effective reasoning frameworks and learning systems based on these technologies to go beyond a mere word-level analysis of natural language text. The Special Issue thus invites the articles depicting the novel concept that generates a more efficient passage from unstructured natural language to knowledge informative data for predictive decision in the field of Tourism and Hospitality. Articles are thus invited on the Development of Tourism using Emerging Technologies, Analysis on after effect of the pandemic on tourism through Machine Learning, Sentiment Analysis, and Emotional Intelligence with the following topics but not limited to:

  • Emotion Mining of Social Media for Tourism;
  • Personalized Recommendations for Effective Tourism using Artificial Intelligence;
  • Sentiment Analysis of Tourist Blogs;
  • The current state of Big Data Research in Tourism Post-Pandemic: Big Data Analytics in Tourism and Hospitality;
  • Role of Para-social interaction in Tourism;
  • Revenue Management in Tourism and Hospitality;
  • Facial Recognition in Tourism using AI;
  • Augmented Reality and AI Trends in Travel Industry;
  • Smart Baggage Handling;
  • Sentiment Analysis in Hospitality and Tourism;
  • Recommendation System based on Topic Modeling and Sentiment Analysis;
  • Text Analytics and Tourism.

Dr. Amit Kumar Mishra
Dr. Alfonso González-Briones
Prof. Dr. Juan M. Corchado
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

18 pages, 4599 KiB  
Article
Route Optimization of Mobile Medical Unit with Reinforcement Learning
Sustainability 2023, 15(5), 3937; https://doi.org/10.3390/su15053937 - 21 Feb 2023
Viewed by 1125
Abstract
In this paper, we propose a solution for optimizing the routes of Mobile Medical Units (MMUs) in the domain of vehicle routing and scheduling. The generic objective is to optimize the distance traveled by the MMUs as well as optimizing the associated cost. [...] Read more.
In this paper, we propose a solution for optimizing the routes of Mobile Medical Units (MMUs) in the domain of vehicle routing and scheduling. The generic objective is to optimize the distance traveled by the MMUs as well as optimizing the associated cost. These MMUs are located at a central depot. The idea is to provide improved healthcare to the rural people of India. The solution is obtained in two stages: preparing a mathematical model with the most suitable parameters, and then in the second phase, implementing an algorithm to obtain an optimized solution. The solution is focused on multiple parameters, including the number of vans, number of specialists, total distance, total travel time, and others. The solution is further supported by Reinforcement Learning, explaining the best possible optimized route and total distance traveled. Full article
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19 pages, 638 KiB  
Article
A Hybrid Model for the Measurement of the Similarity between Twitter Profiles
Sustainability 2022, 14(9), 4909; https://doi.org/10.3390/su14094909 - 19 Apr 2022
Viewed by 1752
Abstract
Social media platforms have been an undeniable part of our lifestyle for the past decade. Analyzing the information that is being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and [...] Read more.
Social media platforms have been an undeniable part of our lifestyle for the past decade. Analyzing the information that is being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and to increase user satisfaction. To draw any further conclusions, first, it is necessary to know how to compare users. In this paper, a hybrid model is proposed to measure the degree of similarity between Twitter profiles by calculating features related to the users’ behavioral habits. For this, first, the timeline of each profile was extracted using the official TwitterAPI. Then, three aspects of a profile were deliberated in parallel. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping was utilized to compare the behavioral ratios of two profiles. Next, the audience network was extracted for each user, and to estimate the similarity of two sets, the Jaccard similarity was used. Finally, for the content similarity measurement, the tweets were preprocessed using the feature extraction method; TF-IDF and DistilBERT were employed for feature extraction and then compared using the cosine similarity method. The results showed that TF-IDF had slightly better performance; it was therefore selected for use in the model. When measuring the similarity level of different profiles, a Random Forest classification model was used, which was trained on 19,900 users, revealing a 0.97 accuracy in detecting similar profiles from different ones. As a step further, this convoluted similarity measurement can find users with very short distances, which are indicative of duplicate users. Full article
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