Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

2022 ◽  
pp. 20-39
Author(s):  
Elliot Mbunge ◽  
Benhildah Muchemwa

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

2020 ◽  
Vol 32 (8) ◽  
pp. 2677-2715
Author(s):  
Hsien-Cheng Lin ◽  
Xiao Han ◽  
Tu Lyu ◽  
Wen-Hsien Ho ◽  
Yunbao Xu ◽  
...  

Purpose Research in tourism and hospitality industry marketing has identified many highly effective applications of social media. However, studies in the existing literature do not enable a comprehensive understanding of this phenomenon because they lack a theoretical foundation. Therefore, this study systematically reviewed the literature from the perspective of the task-technology fit (TTF) theory. The purpose of this paper is to map out what is known about social media use in tourism and hospitality marketing and what areas need further exploration. Design/methodology/approach A descriptive cumulative review of the literature obtained 99 articles published in tourism and hospitality journals from 2010 to 2019. Findings The analysis suggests that to understand social media use in tourism marketing, researchers and practitioners in the industry must clarify the following four issues: the control variables, longitudinal analyzes and TTF concepts that should be used in future studies; the fitness of social media platforms for tourism marketing; how various social media platforms differ in terms of performance outcome; and the digital divide in the use of social media for tourism. Originality/value An integrated framework was developed to identify constructs and to understand their relationships. Recent studies in this domain are discussed; theoretical and practical suggestions and implications for future research are given.


2021 ◽  
Vol 40 ◽  
pp. 03030
Author(s):  
Mehdi Surani ◽  
Ramchandra Mangrulkar

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.


2019 ◽  
Vol 118 (12) ◽  
pp. 130-141
Author(s):  
Khaled Alsardia

Social media is one of the most popular, effective, and accessible means for the communication of information in today’s globalized world. Harnessed as a tool for marketing, it can help exponentially expand businesses by instantly spreading the word about their products and services to countless existing and potential customers. The aim of this research is to study the influence of social media in promoting tourism and hospitality in Jordan. Data were gathered using an online Google Form questionnaire sent out to nearly 150 respondents via WhatsApp and analysed via quantitative methods using SPSS 20.0.


2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Salim Al-Hajri ◽  
Abdelghani Echchabi ◽  
Mohammed Mispah Said Omar ◽  
Abdullah Mohammed Ayedh

In the emerging tourism and hospitality industries such as that of Oman, companies can market their services and products using the Social Media Networks (hereafter SMNs) and engage customers to identify their requirements online. Oman recognizes the benefits of SMNs in the tourism and hospitality industry and it has made major efforts to ensure the success of this newly introduced industry like its neighboring country the United Arab Emirates (hereafter UAE). Even though, the hospitality industry is vital to the economy of Oman, the Omani hospitality industry continues employing the conventional approach while conducting transactions. Understanding the influence of accepting such an innovation in the hospitality industry in Oman raises a fruitful research question to investigate. Therefore, it is this study’s objective to examine the influence of SMNs Acceptance in the tourism and hospitality industry in Oman. For the attainment of the study’s objective, the study uses a survey questionnaire to 200 respondents that have visited Oman recently, where 182 responses were properly filled and returned. The structural equation modeling (hereafter SEM) had been utilized to analyze the collected data. The results reveal that the respondents had high degree of satisfaction with their travel experience and they intended to continue using SMNs for tourism purposes. Nonetheless, it was found that the major factors influencing their decisions are: perceived usefulness, perceived ease of use, subjective norms and reliability.


2016 ◽  
Vol 14 (1) ◽  
pp. 251-258
Author(s):  
Claudette Rabie

In today’s technologically driven and revolutionised era of digital communication, numerous people combine a complex collection of social media platforms and technology to connect them to the world and people around them. The online participation of consumers has forced companies to embrace social media marketing efforts. The continuous advancements in marketing mediums have a major influence on the success and growth of companies, particularly within the tourism and hospitality industry, as increasingly more travellers are using social media as a means of communicating and seeking information. The purpose of this research study was therefore to explore if users of social media as a promotional mix element could be clustered into different groups based on characteristics they possess. A web-based self-administered questionnaire was distributed to accommodation establishments located in the Western Cape province of South Africa and a total of 361 useable responses were received. A four-step cluster analysis was performed in order to identify similar groups of respondents in their use of social media as a promotional mix element. The findings presented four distinct users of social media in the accommodation industry based on six variables identified from the literature.


ICR Journal ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 189-212
Author(s):  
Talat Zubair ◽  
Amana Raquib ◽  
Junaid Qadir

The growing trend of sharing and acquiring news through social media platforms and the World Wide Web has impacted individuals as well as societies, spreading misinformation and disinformation. This trend—along with rapid developments in the field of machine learning, particularly with the emergence of techniques such as deep learning that can be used to generate data—has grave political, social, ethical, security, and privacy implications for society. This paper discusses the technologies that have led to the rise of problems such as fake news articles, filter bubbles, social media bots, and deep-fake videos, and their implications, while providing insights from the Islamic ethical tradition that can aid in mitigating them. We view these technologies and artifacts through the Islamic lens, concluding that they violate the commandment of spreading truth and countering falsehood. We present a set of guidelines, with reference to Qur‘anic and Prophetic teachings and the practices of the early Muslim scholars, on countering deception, putting forward ideas on developing these technologies while keeping Islamic ethics in perspective.


2022 ◽  
Vol 9 ◽  
Author(s):  
Zunera Jalil ◽  
Ahmed Abbasi ◽  
Abdul Rehman Javed ◽  
Muhammad Badruddin Khan ◽  
Mozaherul Hoque Abul Hasanat ◽  
...  

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.


Author(s):  
Eman Bashir ◽  
◽  
Mohamed Bouguessa

Broadly cyberbullying is viewed as a severe social danger that influences many individuals around the globe, particularly young people and teenagers. The Arabic world has embraced technology and continues using it in different ways to communicate inside social media platforms. However, the Arabic text has drawbacks for its complexity, challenges, and scarcity of its resources. This paper investigates several questions related to the content of how to protect an Arabic text from cyberbullying/harassment through the information posted on Twitter. To answer this question, we collected the Arab corpus covering the topics with specific words, which will explain in detail. We devised experiments in which we investigated several learning approaches. Our results suggest that deep learning models like LSTM achieve better performance compared to other traditional yberbullying classifiers with an accuracy of 72%.


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