scholarly journals Mapping problem using text mining to boost tourism industry: is it possible?

2021 ◽  
Vol 778 (1) ◽  
pp. 012009
Author(s):  
M Tamrin ◽  
L Septianasari
Keyword(s):  
Text Mining ◽  
Information Extraction ◽  
Online Reviews ◽  
Tourism Industry ◽  
Support Vector ◽  
Mapping Problem ◽  
Tourist Destinations ◽  
Online Platforms ◽  
Positive Class ◽  
Negative Class

Abstract TripAdvisor has become a credential traveling platform for tourists worldwide to set travel plans. The widespread of big data in online platforms urges the use of text mining to benefit some sectors, including in the tourism industry. This study aimed to investigate the information extraction based on the online reviews on TripAdvisor for Gili Trawangan tourist destinations. The method used in this research was text mining with Support Vector Machine (SVM) to classify the online reviews that categorized into two classes, positive class and negative class. The results of information extraction show that the issue of horse cruelty, bad waste management, and ecosystem vulnerability dominated the negative sentiments. These negative sentiments need to be handled professionally by the tourism enterprise to boost the tourism industry in Gili Trawangan.

Negative Customer Experience in Lifestyle Hotels

2021 ◽  
pp. 126-157
Author(s):  
Manoel Vitor Santos ◽  
Amélia M. P. C. Brandão
Keyword(s):  
Social Networks ◽  
Text Mining ◽  
Consumer Behaviour ◽  
Online Reviews ◽  
Customer Experience ◽  
Customer Complaints ◽  
Online Platforms ◽  
Hotel Service ◽  
Hotel Managers

The primary purpose of the present research is to develop a methodology which can accurately analyse online public reviews on Google using Netnography studies combined with text mining analyses. By analysing the current techniques applied to a lifestyle hotel brand in nine properties in different countries and carefully studying how negative reviews are expressed online by costumers, this study aims to create a pattern of lifestyle customer complaints. This research seeks to demonstrate patterns of consumer behaviour that are not fully satisfied with the hotel service and how it can negatively affect the brand. This study identifies the areas that five stars lifestyle hoteliers and hotel managers need to pay attention to improve services, considering online reviews on online platforms, such as social networks and other tourism sites. Today, online reviews and customer experiences have a significant impact on the choice of a hotel.

Analysis of Online Hotel Reviews During the COVID-19 Pandemic Using Topic Modeling

2021 ◽  
pp. 478-494
Author(s):  
Özlem Ergüt
Keyword(s):  
Text Mining ◽  
Service Quality ◽  
Topic Modeling ◽  
Online Reviews ◽  
Current Situation ◽  
Tourism Industry ◽  
Daily Lives ◽  
New Normal ◽  
Tourism Sector ◽  
The World

The world is facing the COVID-19 pandemic that has impacted economies and millions of people worldwide. The fact that COVID-19 is highly contagious from person to person has greatly affected the daily lives of people, and it has also had a devastating effect on many sectors, particularly the tourism industry. In order to mitigate losses for the tourism sector and for it to gain a new dynamism under the current pandemic conditions, monitoring and analyzing online reviews is an important factor for better understanding the needs and desires of customers. The purpose of this study was to determine the main topics in online reviews by foreign guests staying in İstanbul during the pandemic period using text mining techniques. The information obtained as a result of the analysis is important in terms of understanding how to manage the current situation, developing suggestions for solutions, improving service quality, making future decisions, and adapting to the new normal.

scholarly journals Implementation of Integrated Bayes Formula and Support Vector Machine for Analysing Airline’s Passengers Review

2020 ◽  
Vol 202 ◽  
pp. 15004
Author(s):  
Aditya Tegar Satria ◽  
Mustafid ◽  
Dinar Mutiara Kusumo Nugraheni
Keyword(s):  
Support Vector Machine ◽  
Information System ◽  
Text Mining ◽  
Input Data ◽  
Tourism Industry ◽  
Support Vector ◽  
Accuracy Score ◽  
Bayes Formula ◽  
Performance Results ◽  
Mining Algorithms

Nowadays, the utilization of Internet of Things (IoT) is commonly used in the tourism industry, including aviation, where passengers of flight services can rate their satisfaction levels towards the product and service they use by writing their reviews in the form of text-based data on many popular websites. These passenger reviews are collections of potential big data and can be analyzed in order to extract meaningful informations. Some text mining algorithms are already in common use, including the Bayes formula and Support Vector Machine methods. This research proposes an implementation of the Bayes and SVM methods where these algorithms will operate independently yet integrated with other modules such as input data, text pre-processing and shows output result concisely in one single information system. The proposed system was successfully delivered 1000 documents of passenger reviews as input data, then after implemented the pre-processing method, the Bayes formula was used to classify the document reviews into 5 categories, including plane condition, flight comfort, staff service, food and entertainment, and price. While simultanously, the positive and negative sentiment contained in the review document was analyzed with SVM method and shows the accuracy score of 83.6% for a training to testing set ratio of 50:50, while 82.75% accuracy for the 60:40 ratio, and 83.3% accuracy for the 70:30 ratio. This research shows that two different text mining algorithms can be implemented simultaneously in a effective and efficient way, while still providing an accurate and satisfying performance results in one integrated information system.

P-SMOTE: One Oversampling Technique for Class Imbalanced Text Classification

2011 ◽  
Author(s):  
Jingjing Wang ◽  
Wen Feng Lu ◽  
Han Tong Loh
Keyword(s):  
Text Classification ◽  
Information Source ◽  
Support Vector ◽  
Svm Classifier ◽  
Imbalanced Dataset ◽  
Patent Classification ◽  
Basic Issue ◽  
Positive Class ◽  
Standard Text ◽  
Negative Class

The importance of mining patents to support product design has been recognized, because patents are the major information source to support innovation and contain novel ideas, which usually cannot be found in published academic papers. In patent text mining, a basic issue is patent classification. However, automatic patent classification is difficult. One potential cause of the difficulty is the imbalanced dataset i.e. the interested positive class is minor while uninterested negative class is major. To alleviate the problem of imbalanced dataset and improve the performance of a Support Vector Machine (SVM) classifier, this study proposes P-SMOTE, a new oversampling technique which focuses on the blank spaces along positive borderline of a SVM. The proposed technique was firstly investigated on Reuters-21578, which is a standard text classification dataset. Then, P-SMOTE was applied to a design patent document dataset. It was observed that a SVM classifier with P-SMOTE, compared to a SVM classifier only, successfully achieved better results.

scholarly journals PREDIKSI FITUR KEMASAN PRODUK MINYAK KAYU PUTIH DENGAN SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Vol 4 ◽  
pp. 76-82
Author(s):  
Wilma Latuny ◽  
Victor O. Lawalata ◽  
Daniel B. Paillin ◽  
Rahman Ohoirenan
Keyword(s):  
Support Vector Machine ◽  
Online Survey ◽  
Accurate Method ◽  
Survey Method ◽  
Support Vector ◽  
Consumer Sentiment ◽  
Product Packaging ◽  
Eucalyptus Oil ◽  
Positive Class ◽  
Negative Class

UD Sinar Baru has eucalyptus oil products with various sizes from 30 ml to 550 ml, and the size of 550 ml is the most consumed eucalyptus oil product. However, this product has been criticized by consumers for its packaging which has not met their expectations. This study aims to obtain an accurate method of classifying consumer sentiment and obtain features that affect the redesign of the 550 ml eucalyptus oil product packaging. Collecting data using an online survey method from social media Facebook to get consumer comments using power queries. Data analysis uses the concept of the Support Vector Machine (SVM) method with the support of the WEKA application to provide sentiment analysis and accuracy of consumer comments. The results of the study present the tendency of comments on each attribute with an assessment of 83% accuracy for the entire class, 3% for positive class comments, and 57% comments for negative class. The sentiment that shows the packaging tends to be normal at 20% which is interpreted as neutral. The conclusion from the results of this study is that SMO has a very accurate prediction rate to analyze consumer sentiment about the features of the 550 ml eucalyptus oil packaging, and it is necessary to redesign the current packaging by considering the features of shape, color, size, and efficiency.

scholarly journals SENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI’S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD

2021 ◽  
Vol 4 (1) ◽  
pp. 17-22
Author(s):  
Zetta Nillawati Reyka Putri ◽  
Muhammad Muhajir
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Test Results ◽  
Text Data ◽  
Machine Method ◽  
Svm Classification ◽  
The Public ◽  
Positive Class ◽  
Negative Class

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.

ETHNIC CULTURE OF LAMUNKHINSKI EVENS AS A RESOURCE OF DEVELOPMENT OF TOURISM INDUSTRY OF YAKUTIA

2020 ◽  
Vol 4 (8(77)) ◽  
pp. 4-7
Author(s):  
Sardaana Anatolievna Alekseeva
Keyword(s):  
National Culture ◽  
Local Group ◽  
Tourism Industry ◽  
Cultural Development ◽  
Ethnic Tourism ◽  
Tourist Destinations ◽  
The North ◽  
Original Culture ◽  
The Village ◽  
Hunting And Fishing

When getting acquainted with the ethnic traditions of the peoples of Yakutia, special attention should be paid to the national culture of the evens as a small indigenous people of the North. Cultural and ethnographic features of Yakutia are one of the most important resources for the development of tourism. The main purpose of the work is to consider the potential of ethnic tourism on the example of the village of Sebyan-Kuel in the Кobyai district of Yakutia. The following specific ethnographic methods are used: the method of included observation and indepth interview. The result was that in this remote mountains of the Verkhoyansk ridge preserved the original culture of the local group Lamynkhinsky Evens, which is a unique, non-commodity, and, consequently, an inexhaustible resource for the economy, social and cultural development of the nasleg. In our opinion, the area of Lamynkhinsky nasleg can become one of the most popular tourist destinations due to its uniqueness in ethnic and extreme, ecological, hunting and fishing types of tourism.

Quality of Life (QOL) of Balinese Women Working in Spa Tourism Industry

2015 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Putu Sucita Yanthy ◽  
Luh Gede Leli Kusuma Dewi ◽  
W. Citra Juwitasari
Keyword(s):  
Quality Of Life ◽  
Tourism Industry ◽  
Family Needs ◽  
Interesting Phenomenon ◽  
Tourist Destinations ◽  
Foreign Countries ◽  
The Family ◽  
Two Factors ◽  
Material Factor

Bali is one of spa tourist destinations having various categories of spas and spa treatments, and the most important is the spa therapists. Spa development becomes an interesting phenomenon to be studied when it is associated with an involvement of Balinese women as spa therapists in foreign countries. The world’s demand for Balinese spa therapists has become the motivation of women to work in this area. The work and life of Balinese spa therapists while they are working in foreign countries serve as parameters to know their quality of life, and these parameters are also the main focus of this study. Through in-depth interviews and questionnaires distributed to 20 therapists it was found out that 85 percent of them have revealed an improvement in their quality of life that is influenced by two factors: the material and intimacy factors. The material factor in question refers to the economic improvement of the family as they could earn enough income to cover their family needs. The intimacy factor in question refers to closeness and a sense of solidarity fostered while they are working abroad and the relationship within the family. This study concludes that the most important part of the development of spa in Bali is its female Balinese spa therapists due to the image that Balinese women working as spa therapists are loyal, hard-working and honest making them in demand among tourists who are seeking spa treatments. Being a spa therapist can improve their quality of life, which means that subjectively both material and intimacy factors are the aspects that affect the quality of life of the Balinese spa therapists.

Positioning of tourist destinations in the digital era: a review of online customer feedback

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gaurav Tripathi ◽  
Parul Wasan
Keyword(s):  
Digital Media ◽  
Tourism Industry ◽  
Destination Image ◽  
Site Management ◽  
Visitor Experience ◽  
Content Type ◽  
Customer Reviews ◽  
Tourist Destinations ◽  
Customer Feedback ◽  
Thoughtful Discussion

Purpose The purpose of this paper is to identify features of online content that create engagement amongst consumers by exploring online customer feedback from the world’s leading tourist website (s). This paper also attempts to unveil the factors based on customer reviews, which will be vital for the tourism industry professionals to promote and position India’s tourist destinations. Design/methodology/approach This paper involves an analysis of customer feedback from TripAdvisor.com. The approach to research is exploratory and attempts to uncover critical factors arising out of rising visitor experience in the digital media sphere. Findings Key factors are nuanced around service quality of the destination image. Identified factors that need the attention of the policymakers, site management and service professionals at large are fairness of price, distractions/irritants and varied expectations of the international and national tourists. Practical implications The findings will have substantial implications for the policymakers, the site management and service professionals. Research outcomes are based on the analysis of real customer reviews hence makes this study vital for decision-makers as well as for academic researchers working in this area. Originality/value This study used the real tourist’s data from TripAdvisor.com. The customer’s postings on the website are those of verified visitors. This paper should help in developing a thoughtful discussion around positioning India as a preferred destination in the online arena aiming at future tourists.

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