Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyan Luo ◽  
Tao Tong ◽  
Xiaoxu Zhang ◽  
Zheng Yang ◽  
Ling Li
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Small Sample ◽  
Destination Image ◽  
Tourism Destination ◽  
Fuzzy Algorithm ◽  
Content Type ◽  
Dirichlet Allocation

PurposeIn the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.Design/methodology/approachThe study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.FindingsIn the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.Originality/valuePrevious research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

Mining numerical measure of consumers’ product evaluation expressed in words based on latent Dirichlet allocation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziang Wang ◽  
Feng Yang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Product Evaluation ◽  
Online Reviews ◽  
Product Evaluations ◽  
Product Attributes ◽  
Data Set ◽  
Content Type ◽  
Face To Face ◽  
Online Retailers ◽  
Dirichlet Allocation

Purpose It has always been a hot topic for online retailers to obtain consumers’ product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between online retailers and customers. After collecting online reviews left by customers, online retailers are eager to acquire answers to some questions. For example, which product attributes will attract consumers? Or which step brings a better experience to consumers during the process of shopping? This paper aims to associate the latent Dirichlet allocation (LDA) model with the consumers’ attitude and provides a method to calculate the numerical measure of consumers’ product evaluation expressed in each word. Design/methodology/approach First, all possible pairs of reviews are organized as a document to build the corpus. After that, latent topics of the traditional LDA model noted as the standard LDA model, are separated into shared and differential topics. Then, the authors associate the model with consumers’ attitudes toward each review which is distinguished as positive review and non-positive review. The product evaluation reflected in consumers’ binary attitude is expanded to each word that appeared in the corpus. Finally, a variational optimization is introduced to calculate parameters mentioned in the expanded LDA model. Findings The experiment’s result illustrates that the LDA model in the research noted as an expanded LDA model, can successfully assign sufficient probability with words related to products attributes or consumers’ product evaluation. Compared with the standard LDA model, the expanded model intended to assign higher probability with words, which have a higher ranking within each topic. Besides, the expanded model also has higher precision on the prediction set, which shows that breaking down the topics into two categories fits better on the data set than the standard LDA model. The product evaluation of each word is calculated by the expanded model and depicted at the end of the experiment. Originality/value This research provides a new method to calculate consumers’ product evaluation from reviews in the level of words. Words may be used to describe product attributes or consumers’ experiences in reviews. Assigning words with numerical measures can analyze consumers’ products evaluation quantitatively. Besides, words are labeled themselves, they can also be ranked if a numerical measure is given. Online retailers can benefit from the result for label choosing, advertising or product recommendation.

scholarly journals Characterizing Tourism Destination Image Using Photos’ Visual Content

2020 ◽  
Vol 9 (12) ◽  
pp. 730
Author(s):  
Xin Xiao ◽  
Chaoyang Fang ◽  
Hui Lin
Keyword(s):  
Deep Learning ◽  
Visual Information ◽  
Latent Dirichlet Allocation ◽  
Hot Spot ◽  
Small Sample Size ◽  
Small Sample ◽  
Destination Image ◽  
Learning Technology ◽  
Visual Content ◽  
Tourism Destination

“A picture is worth a thousand words”. Analysis of the visual content of tourist photos is an effective way to explore the image of tourist destinations. With the development of computer deep learning and big data mining technology, identifying the content of massive numbers of tourist photos by convolutional neural network (CNN) approaches breaks through the limitations of manual approaches of identifying photos’ visual information, e.g., small sample size, complex identification process, and results deviation. In this study, 531,629 travel photos of Jiangxi were identified as 365 scenes through deep learning technology. Through the latent Dirichlet allocation (LDA) model, five major tourism topics are found and visualized by map. Then, we explored the spatial and temporal distribution characteristics of different tourism scenes based on hot spot analysis technology and the seasonal evaluation index. Our research shows that the visual content mining on travel photos makes it possible to understand the tourism destination image and to reveal the temporal and spatial heterogeneity of the image, thereby providing an important reference for tourism marketing.

Tourism destination image based on tourism user generated content on internet

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jun Wang ◽  
Yunpeng Li ◽  
Bihu Wu ◽  
Yao Wang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Three Dimensional ◽  
Transformation Model ◽  
Destination Image ◽  
User Generated Content ◽  
Tourism Destination ◽  
Methodological Innovation ◽  
Content Type ◽  
Social Sensing

Purpose The purpose of this paper is to study tourists’ spatial and psychological involvement reflected through tourism destination image (TDI), TDI is divided into on-site and after-trip groups and the two groups are compared in the frame of three-dimensional continuums. Design/methodology/approach By conducting latent Dirichlet allocation (LDA) modeling to tourism user-generated content, structural topic models are established. The topics separated out from unstructured raw texts are structural themes and representations of TDI. Social network analysis (SNA) reveals the quantitative and structural differences of three-dimensional continuums of the two TDI groups. Findings The findings reveal that from the stage of on-site to after-trip, tourist perception of TDI shifts from psychologically to functionally-oriented, from common to unique, and from holistic to more attribute focused. Also, it is suggested that from a postmodernism perspective, TDI is never unique, fixed or universal, but has different image perceptions and feedbacks for different tourists. Research limitations/implications With the assistance of social sensing, a panoramic view of TDI could be established. Targeted and precision destination marketing and image promotion could be applied out to each individual tourist. Originality/value Combining with the perspectives of the tourist-destination space system and the tourism involvement theory, this research proposes a TDI transformation model and an explanation of the internal mechanism. The originality of research also lies in the methodological innovation of social sensing data and the LDA topic model.

Like, tag and share: bolstering social media marketing to improve intention to visit a nature-based tourism destination

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vanessa Gaffar ◽  
Benny Tjahjono ◽  
Taufik Abdullah ◽  
Vidi Sukmayadi
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Botanical Garden ◽  
Equation Model ◽  
Destination Image ◽  
Social Media Marketing ◽  
Tourism Destination ◽  
Content Type ◽  
Future Value ◽  
Tourism Destinations

Purpose This paper aims to explore the influence of social media marketing on tourists’ intention to visit a botanical garden, which is one of the popular nature-based tourism destinations in Indonesia. Design/methodology/approach This study sent questionnaires to 400 followers of the botanical garden’s Facebook account who responded to the initial calls for participation and declared that they have not visited the garden before. Analyses were conducted on 363 valid responses using the structural equation model. Findings The findings revealed several key determinants influencing the image of the botanical garden and its future value proposition, particularly in supporting the endeavour to shift from a mere recreational destination to a nature-based tourism destination offering educational experiences. Originality/value This paper offers a fresh look into the roles of social media marketing in increasing the intention to visit a tourism destination that is considerably affected by the destination image.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals A Latent Dirichlet Allocation and Fuzzy Clustering Based Machine Learning Model for Text Thesaurus

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Jia Luo ◽  
Dongwen Yu ◽  
Zong Dai
Keyword(s):  
Machine Learning ◽  
Fuzzy Clustering ◽  
Latent Dirichlet Allocation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Text Data ◽  
Huge Data ◽  
Machine Learning Model ◽  
N Gram ◽  
Dirichlet Allocation

It is not quite possible to use manual methods to process the huge amount of structured and semi-structured data. This study aims to solve the problem of processing huge data through machine learning algorithms. We collected the text data of the company’s public opinion through crawlers, and use Latent Dirichlet Allocation (LDA) algorithm to extract the keywords of the text, and uses fuzzy clustering to cluster the keywords to form different topics. The topic keywords will be used as a seed dictionary for new word discovery. In order to verify the efficiency of machine learning in new word discovery, algorithms based on association rules, N-Gram, PMI, andWord2vec were used for comparative testing of new word discovery. The experimental results show that the Word2vec algorithm based on machine learning model has the highest accuracy, recall and F-value indicators.

The internationalization strategies of rapidly internationalizing high-tech UK SMEs

2014 ◽  
Vol 26 (5) ◽  
pp. 421-448 ◽  
Author(s):  
Dave Crick ◽  
James Crick
Keyword(s):  
Decision Making ◽  
Small And Medium Enterprises ◽  
Potential Effect ◽  
Small Sample ◽  
High Tech ◽  
Niche Markets ◽  
Content Type ◽  
Parental Decision Making ◽  
Internationalization Strategies ◽  
Medium Enterprises

Purpose – The purpose of this paper is to investigate aspects of causation and effectuation decision-making in respect of the planned and unplanned nature of the internationalization strategies of a small sample of rapidly internationalizing, high-tech UK small and medium enterprises (SMEs). These exhibit four different rates of scale of international intensity (percentage of overseas sales to total sales) and market scope (geographical coverage and commitment). Design/methodology/approach – Interviews with managers of 16 independently owned high-technology-oriented manufacturing SMEs were undertaken in this investigation to reduce the potential effect of bias from parental decision-making and firm size, also trade sectoral conditions. These were drawn from an existing database. Findings – Aspects of both causation and effectuation logic were evident in planned and unplanned aspects of decision-making. Moreover, industry factors were seen to affect internationalization strategies in various ways and not least in respect of the need to exploit windows of opportunity in international niche markets and the usefulness of utilizing managers’ experience and networks in the sector in which firms operated. Originality/value – The contribution of this study is to build on earlier work where authors have used different terminology to describe firms that have internationalized soon after their foundation. Specifically, with respect to the planned versus unplanned nature of respective internationalization strategies and the causation as opposed to effectuation logic in decision-making.

Exploring energy-saving refrigerators through online e-commerce reviews: an augmented mining model based on machine learning methods

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyan Luo ◽  
Zheng Yang ◽  
Yuan Liang ◽  
Xiaoxu Zhang ◽  
Hong Xiao
Keyword(s):  
Machine Learning ◽  
Energy Saving ◽  
Online Reviews ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Methods ◽  
Model Based ◽  
Review Mining ◽  
Strategy Model ◽  
Mining Model

PurposeBased on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.Design/methodology/approachThis study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.FindingsBased on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.Research limitations/implicationsIn terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.Originality/valueThis study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

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