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ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

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.

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.

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.

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.

During crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online. However, relevant content can be disseminated in document streams. The available information can also contain redundant content published by different sources. Therefore, the need of automatic construction of summaries that aggregate important, non-redundant and non-outdated pieces of information is becoming critical.

The aim of this paper is to present a new temporal summarization approach based on a popular topic model in the information retrieval field, the Latent Dirichlet Allocation. The approach consists of filtering documents over streams, extracting relevant parts of information and then using topic modeling to reveal their underlying aspects to extract the most relevant and novel pieces of information to be added to the summary.

The performance evaluation of the proposed temporal summarization approach based on Latent Dirichlet Allocation, performed on the TREC Temporal Summarization 2014 framework, clearly demonstrates its effectiveness to provide short and precise summaries of events.

Unlike most of the state of the art approaches, the proposed method determines the importance of the pieces of information to be added to the summaries solely relying on their representation in the topic space provided by Latent Dirichlet Allocation, without the use of any external source of evidence.

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

Studies in higher education tend to use different methods and methodologies, from documentary analysis to auto/biographical and observational studies. Most studies are either qualitative or qualitative. A mixed-methods approach has emerged in recent years, in which the qualitative approach generally plays an important role. The purpose of this chapter is to show the potential of a new methodology that is also appropriate for higher education research and widely used in the social sciences: probabilistic topic models. A probabilistic method can be used to analyse and categorise thousands of words. After collecting large sets of texts, content analysis is used to deeply analyse the meaning of these words. The huge number of texts published today pushes researchers to employ new techniques in their search for hidden structures built upon a set of core ideas. These methods are called topic modelling algorithms, with Latent Dirichlet Allocation being the basic probabilistic topic model. The application of these new techniques to the field of higher education is extremely useful, for two reasons: (1) studies in this area deal in some cases with a great volume of data and (2) these techniques allow one to devise models in a way that is unsupervised by humans (even when researchers operate on the resulting model); thus they are less subjective than other types of analyses and methods used for qualitative purposes. This chapter shows the foundations and recent applications of the technique in the higher education field, as well as challenges related to this new technique.

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.

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.

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.

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.

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.

本研究针对旅游目的地形象（TDI）及其体现出的游客空间和心理涉入, 将旅游目的地形象划分为在场形象和游后形象, 并将二者在TDI三维连续体（Three-dimensional continuums）框架下进行比较。

本研究应用内容分析法, 通过对旅游用户生成内容（tourism UGC）进行LDA（Latent Dirichlet Allocation）建模, 从非结构化的原始文本中建立起结构化的语义主题模型, 并且应用社会网络分析（Social Network Analysis）, 从定量和结构化的角度揭示了游中与游后目的地形象的差异。

研究发现, 从游中到游后, 游客的目的地形象感知经历了从心理到功能、从一般到特殊、从整体到属性的转变。同时, 基于后现代主义的视角, 旅游目的地形象并不是唯一的、固定的或放之四海而皆准的, 而是在不同的游客感知中有不同的形象和体现。

应用社会感知（Social Sensing）理论可以全面解析旅游目的地形象。同时可以针对特定游客采取精准定点的旅游目的地营销和形象推广手段。

本研究从旅游目的地空间系统和旅游涉入理论视角出发, 提出了旅游目的地形象转变的模型和其内在机制解释, 在方法上创新性地使用了社会感知数据和LDA主题模型。

关键词 旅游目的地形象, 在场形象, 游后形象, 旅游用户生成内容 (tourism UGC), LDA（Latent Dirichlet Allocation）建模, 社会感知

Para estudiar el grado de participación espacial y psicológica de los turistas reflejado en la imagen del destino turístico (TDI), el TDI se divide en grupo en el sitio y grupo posterior al viaje, y los dos grupos se comparan en el marco del continuo tridimensional.

Al modelar la posible asignación de Dirichlet (LDA) del contenido generado por el usuario turístico (UGC), se estableció un modelo de tema estructural. El tema que está separado del texto original no estructurado es el tema estructurado y la representación de TDI. El análisis de redes sociales reveló diferencias en el número y la estructura de los continuos tridimensionales de los dos grupos de TDI.

Los resultados de la encuesta muestran que, desde la escena hasta los viajes, la percepción de los turistas de TDI cambia de orientación psicológica a funcional, de lo ordinario a lo único, y de una atención general a más. Además, se sugiere que desde una perspectiva posmoderna, TDI nunca es único, fijo o universal, sino que tiene diferentes percepciones de imagen y comentarios para diferentes visitantes.

Con la ayuda de la detección social, se podría establecer una vista panorámica de TDI. El marketing de destino y la promoción de imágenes dirigidos y precisos podrían aplicarse a cada turista individual.

Combinando con las perspectivas del sistema espacial de destino turístico y la teoría de la participación turística, esta investigación propone un modelo de transformación TDI y la explicación del mecanismo interno. La originalidad de la investigación también radica en la innovación metodológica de los datos de detección social y el modelo de tema LDA.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

It has been eight months into the global pandemic health crises COVID-19, yet the severity of the crises is just getting worse in many parts of the world. At this stage, it is essential to understand and observe the general attitude of the public toward COVID crises and the major concerns the public has voiced out and how it varies across months. Understanding the impact that the COVID-19 crises have created also helps policymakers and health-care organizations access the primary steps that need to be taken for the welfare of the community. The purpose of this study is to understand the general public's response towards COVID-19 crises and the major issues that concerns them.

For the analysis, data were collected from Twitter. Tweets regarding COVID-19 crises were collected from February 1, 2020, to June 27, 2020. In all, 433,195 tweets were used for this study. Natural language processing (NLP), which is a part of Machine learning, was used for this study. NLP was used to track the changes in the general public's sentiment toward COVID-19 crises and LDA was used to understand the issues that shape the general public's sentiments the crises time. Using Python library Wordcloud, the authors further derived how the primary concerns regarding COVID crises various from February to June of the year 2020.

This study was conducted in two parts. Study 1 results showed that the attitude of the general public toward COVID crises was reasonably neutral at the beginning of the crises (Month of February). As the crises become severe, the sentiments toward COVID increasingly become negative yet a considerable percentage of neutral sentiments existed even at the peak time of the crises. Study 2 finds out that issues including the severity of the disease, Precautionary measures need to be taken, and Personal issues like unemployment and traveling during the pandemic time were identified as the public's primary concerns.

The research adds value to the literature on understanding the major issues and concerns, the public voices out about the current ongoing pandemic. To the best of the authors’ knowledge, this is the first study with an extended period of timeframe (Five months). In this research, the authors have collected data till June for analysis that makes the results and findings more relevant to the current time.

The purpose of this paper is to develop a predictive model for box office performance based on the textual information in movie scripts in the green-lighting process of movie production.

The authors use Latent Dirichlet Allocation to determine the hidden textual structure in movie scripts by extracting topic probabilities as predictors for classification. The extracted topic probabilities are used as inputs for the predictive model for the box office performance. For the predictive model, the authors utilize a variety of classification algorithms such as logistic classification, decision trees, random forests, k-nearest neighbor algorithms, support vector machines and artificial neural networks, and compare their relative performances in predicting movies' market performance.

This approach for extracting textual information from movie scripts produces a valuable typology for movies. Moreover, our modeling approach has significant power to predict movie scripts' profitability. It provides a superior prediction performance compared to previous benchmarks, such as that of Eliashberg et al. (2007).

This work contributes to literature on predicting the box office performance in the green-lighting process and literature regarding suggesting models for the idea screening stage in the new product development process. Besides, this is one of the few studies that use movie script data to predict movies' financial performance by proposing an approach to integrate text mining models and machine learning algorithms with movie experts' intuition.

First, the authors’ approach can significantly reduce the financial risk associated with movie production decisions before the pre-production stage. Second, this paper proposes an approach that is applicable at a very early stage of new product development, such as the idea screening stage. The authors also introduce an online-based movie scenario database system that can help movie studios make more systematic and profitable decisions in the green-lighting process. Third, this approach can help movie studios estimate movie scripts' financial value.

This study is one of the few studies to forecast market performance in the green-lighting process.