scholarly journals Insights from sentiment analysis to leverage local tourism business in restaurants

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ting Yu ◽  
Paulo Rita ◽  
Sérgio Moro ◽  
Cristina Oliveira
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Bench Mark ◽  
Quality Factors ◽  
Vast Number ◽  
Content Type ◽  
Food And Beverage

Purpose Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry. Design/methodology/approach The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value. Findings The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management. Originality/value The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.

Determining banking service attributes from online reviews: text mining and sentiment analysis

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Divya Mittal ◽  
Shiv Ratan Agrawal
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Banking Sector ◽  
Online Reviews ◽  
Socioeconomic Background ◽  
Content Type ◽  
Customer Reviews ◽  
Banking Service ◽  
The Interest Rate

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

Assessing technology adoption practices in Chinese theme parks: text mining and sentiment analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tingting Zhang ◽  
Bin Li ◽  
Ady Milman ◽  
Nan Hua
Keyword(s):  
Text Mining ◽  
Technology Adoption ◽  
Sentiment Analysis ◽  
Statistical Tests ◽  
Online Reviews ◽  
Theme Park ◽  
Theme Parks ◽  
Content Type ◽  
Customer Reviews ◽  
Technology Applications

Purpose This study aims to examine technology adoption practices in Chinese theme parks by leveraging text mining and sentiment analysis approaches on actual theme park customers’ online reviews. Design/methodology/approach The study text mined a total of 65,518 reviews of 490 Chinese theme parks with the aid of the Python program. Further, it computed sentiment scores of the customer reviews associated with the ratings of each categorized technology practice applied in the theme parks. Findings The study identified two major categories of technology applications in theme parks: supporting and experiential technologies. Multiple statistical tests confirmed that supporting technologies consisted of three types: intelligent services, ticketing and in-park transportation. Experiential technologies further included five aspects of technologies according to Schmitt’s strategic experiential modules (SEMs): sense, feel, act, think and relate. Originality/value The study findings contribute to the current understanding of theme park visitors’ perceptions of technology adoption practices and provide insightful implications for theme park practitioners who intend to invest in high technology solutions to deliver a better customer experience.

scholarly journals Improving international attractiveness of higher education institutions based on text mining and sentiment analysis

2018 ◽  
Vol 32 (3) ◽  
pp. 431-447 ◽  
Author(s):  
Carolina Leana Santos ◽  
Paulo Rita ◽  
João Guerreiro
Keyword(s):  
Higher Education ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Design Methodology ◽  
Higher Education Institutions ◽  
Online Reviews ◽  
Content Type ◽  
International Environment ◽  
Depth Analysis ◽  
Support Students

Purpose The increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the internet. HEIs also benefit from such information online. The purpose of this paper is to provide an understanding of the drivers of HEIs success online. Design/methodology/approach Due to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analysing such information manually. The present paper uses text mining (TM) and sentiment analysis (SA) to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with Association to Advance Collegiate Schools of Business accreditation. Findings Results show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment. Research limitations/implications Despite the use of a major platform with a broad number of reviews from students around the world, other sources focussed on other types of HEIs may have been used to reinforce the findings in the current paper. Originality/value The study pioneers the use of TM and SA to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions.

A 360-Degree View of a Hospital by Analysing Patient’s Online Reviews Using Fuzzy Sentiment Analysis

2021 ◽  
pp. 097206342110320
Author(s):  
Alekh Gour ◽  
Sony Kumari
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
New Delhi ◽  
Analysis Model ◽  
Medical Sciences ◽  
Medical College ◽  
Analysis Strategy ◽  
Social Media Platforms

Millions of people use Internet for developing new skills, booking online tickets, socialising, etc. Out of the sundry activities, giving online reviews by customers has become very customary these days and the fastest medium to make one’s voice heard. With the advent of analytics, more specifically, text mining, the online reviews of the customers have made a huge difference in shaping the future strategies of the companies and have also helped them to study the customer responses of their rivals. In an effort to help hospitals analyse the patient’s reviews present online on various social media platforms, this paper analyses the 659 reviews of people across the nation, on one of the best medical college and hospital of India, All India Institute of Medical Sciences, New Delhi. An attempt is made in this article to develop fuzzy sentiment analysis model with integration of naïve base classifier, which helps to analyse reviews of different hospitals and can come up with their own social media competitive analysis strategy. The results reveal the value text mining can bring to the table for any hospital and the immense business value that it holds.

Twitter sentiment analysis of app based online food delivery companies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shrawan Kumar Trivedi ◽  
Amrinder Singh
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Competitive Analysis ◽  
Word List ◽  
Food Delivery ◽  
Content Type ◽  
Sentiment Lexicon ◽  
Analysis Strategy ◽  
Sentiment Dictionary

Purpose There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies. Design/methodology/approach Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform. Findings Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided. Research limitations/implications The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy. Originality/value Twitter analysis of food-based companies has been performed.

The asymmetric effect of review valence on numerical rating

2019 ◽  
Vol 43 (2) ◽  
pp. 283-300 ◽  
Author(s):  
Hsiu-Yuan Tsao ◽  
Ming-Yi Chen ◽  
Hao-Chiang Koong Lin ◽  
Yu-Chun Ma
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Negative Impact ◽  
Online Reviews ◽  
Data Set ◽  
Brand Strength ◽  
Content Type ◽  
Numerical Rating ◽  
The Impact ◽  
Moderating Role

PurposeThe basic assumption is that there is a symmetric relationship between review valence and rating, but what if review valence and rating were linked asymmetrically? There are few studies which have investigated the situations in which positive and negative online reviews exert different influences on ratings. This study considers brand strength as having an important moderating role because the average rating of existing reviews for a particular product is a heuristic cue for decision makers. Thus, the purpose of this paper is to argue that an asymmetric relationship between review content valence and numerical rating will depend on brand strength.Design/methodology/approachThe authors have conducted a sentiment analysis via text mining, using self-developed computer programs to retrieve a data set from the TripAdvisor website.FindingsThis study finds there is an asymmetric relationship between review valence (verbal) and numerical rating. The authors further find brand strength to have an important moderating role. For a stronger brand, negative review content will have a greater impact on numerical ratings than positive review content, while for a weaker brand, positive review content will have a greater impact on numerical ratings than negative review content.Practical implicationsMarketers could adopt sentiment analysis via text mining of online reviews as a valid measure or predictor of consumer satisfaction or numerical ratings. Strong brands should direct more attention to negative reviews, because in such reviews the negative impact transcends the positive. In contrast, weak brands should aim to exploit as many positive reviews as possible to minimize the impact of any negative reviews.Originality/valueThis study finds there is an asymmetric relationship between review valence (verbal) and numerical rating and considers brand strength to play an important moderating role. The authors have used real data from the TripAdvisor website, which allow people to express themselves in an unsolicited manner, and linked these with the results from the sentiment analysis.

A new approach of social media analytics to predict service quality: evidence from the airline industry

2019 ◽  
Vol 33 (1) ◽  
pp. 51-70 ◽  
Author(s):  
Xin Tian ◽  
Wu He ◽  
Chuanyi Tang ◽  
Ling Li ◽  
Hangjun Xu ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Service Quality ◽  
Sentiment Analysis ◽  
Airline Industry ◽  
Social Media Analytics ◽  
Data Set ◽  
Social Media Data ◽  
Content Type ◽  
Media Data

Purpose Research on how to use social media data to measure and evaluate service quality is still limited. To fill the research gap in the literature, the purpose of this paper is to open a new avenue for future work to measure the service quality in the service industry by developing a new analytical approach of using social media analytics to evaluate service quality. Design/methodology/approach This paper uses social media data to measure the service quality of the airline industry with the SERVQUAL metrics. A novel benchmark data set was created for each SERVQUAL metric. The data set was analyzed through text mining and sentiment analysis. Findings By comparing the results from social media with official service quality report from the Department of Transportation, the authors found that the proposed service quality metrics from social media are valid and can be used to estimate the service quality. Practical implications This paper presents service quality metrics and a methodology that can be easily adopted by other businesses to assess service quality. This study also provides guidance and suggestions to help businesses understand how to collect and analyze social media data for the purpose of evaluating service quality. Originality/value This paper offers a novel methodology that uses text mining and sentiment analysis to help the airline industry assess its service quality.

scholarly journals Social media and sentiment in bioenergy consultation

2016 ◽  
Vol 10 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Victoria Uren ◽  
Daniel Wright ◽  
James Scott ◽  
Yulan He ◽  
Hassan Saif
Keyword(s):  
Social Media ◽  
New Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Public Consultation ◽  
Short Paper ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

Sharing tourism experiences in social media

2017 ◽  
Vol 29 (1) ◽  
pp. 179-225 ◽  
Author(s):  
Marios D. Sotiriadis
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Academic Research ◽  
Online Reviews ◽  
Content Type ◽  
Main Research ◽  
Research Themes ◽  
Consumer Perspective ◽  
Use Of Social Media ◽  
The Impact

Purpose The purpose of this paper is twofold: to perform a synthesis of academic research published between 2009 and 2016 regarding the changes in tourism consumer behavior brought about by the use of social media (SM); and to suggest a set of strategies for tourism businesses to seize opportunities and deal with resulting challenges. Design/methodology/approach A volume of 146 peer-reviewed journal articles were retrieved from two major databases. Content analysis of this academic research has been performed, exploring the effects of online reviews on tourism consumers and providers. Findings The content analysis identified three main research themes that were investigated by scholars and classified into two major categories, namely, consumer perspective and provider perspective: the antecedents (factors motivating and influencing tourists); the influence of online reviews on consumer behaviour; and the impact of these reviews on tourism businesses (providers’ perspective). Research limitations/implications This study is based on a literature review and outcomes reported by previous studies; hence, the suggestions are indicative rather than conclusive. Some publication sources were not included. Practical implications This paper suggests a range of adequate strategies, along with operational actions, formulated for industry practitioners in the fields of management and marketing. Originality/value It provides an update of the state of published academic research into SM and an integrated set of management and marketing strategies for tourism providers in seizing the opportunities and dealing with the challenges raised in a digital context.

“Why am I to blame when the law is on my side?” A study of crises, public opinion and frames

2015 ◽  
Vol 23 (4) ◽  
pp. 363-373 ◽  
Author(s):  
Lise-Lotte Holmgreen
Keyword(s):  
Social Media ◽  
Restaurant Industry ◽  
Content Type ◽  
Media Frames ◽  
The Public ◽  
Theoretical Approaches ◽  
Media Constructions ◽  
Organisational Behaviour ◽  
Conceptual Nature ◽  
Insight Into

Purpose – The purpose of this paper is to discuss why social media frames may exert substantial influence on the image of organisations and even trigger organisational crises. Design/methodology/approach – The study applies the theoretical approaches of crisis, framing and stakeholder theory to examine social media constructions of organisational behaviour. A recent case from the Danish restaurant industry exemplifies the structuring of social media frames and their impact on organisational image. Findings – The results of the study confirm the findings of previous studies but with the crucial addition that the power of social media frames is closely connected to their drawing on basic cultural and social beliefs that unite stakeholders across potentially different interests and identities. Research limitations/implications – The study is qualitative and applies a small dataset. To confirm the findings, further studies need to be conducted. Social implications – This paper sheds light on an issue which continues to be highly relevant for organisations. By gaining insight into the conceptual nature of frames and stakeholder motivations, which guide social-media entries, they may be better equipped for meeting the demands of the public and thus for preventing crises. Originality/value – This is a field of research that continues to develop concurrently with the development and spread of social media. By analysing in detail how frames are constructed, the study contributes to research in the field.

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