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The objective of this study was to build an international evaluation index system of cross-border e-commerce brands. It improves the sustainable development ability of the brand and then drives the sustainable development of the enterprise’s brand internationalization. As the top priority in the innovative development process of cross-border e-commerce enterprises, it is a key means to achieve longer-term and more stable development of cross-border enterprises and to maintain a place in the fiercely competitive market. Therefore, the internationalization of cross-border e-commerce brands is a topic that needs to be explored in depth to provide a comprehensive understanding to businesses from the consumers’ perspectives. This study constructs an international evaluation index system for cross-border e-commerce brands. The keywords in the online reviews are captured through the Latent Dirichlet Allocation (LDA) and matched to the indexes, and the indicators are classified into Kano categories through Long Short-Term Memory (LSTM) training to explore the promotion strategies of different Kano categories in the process of brand internationalization. Based on the empirical analysis of online reviews of the Kano model, it was determined that in the process of internationalization of cross-border e-commerce brands, managers should focus on service indicators related to expected factors, give priority to meeting service indicators related to essential factors, strive to meet service indicators related to charm factors, and make appropriate choices to observe service indicators related to indifference factors in real-time.

Recently, global climate change has led to a high incidence of extreme weather and natural disasters. How to reduce its impact has become an important topic. However, the studies that both consider the disaster’s real-time geographic information and environmental factors in severe rainstorms are still not enough. Volunteered geographic information (VGI) data that was generated during disasters offered possibilities for improving the emergency management abilities of decision-makers and the disaster self-rescue abilities of citizens. Through the case study of the extreme rainstorm disaster in Zhengzhou, China, in July 2021, this paper used machine learning to study VGI issued by residents. The vulnerable people and their demands were identified based on the SOS messages. The importance of various indicators was analyzed by combining open data from socio-economic and built-up environment elements. Potential safe areas with shelter resources in five administrative districts in the disaster-prone central area of Zhengzhou were identified based on these data. This study found that VGI can be a reliable data source for future disaster research. The characteristics of rainstorm hazards were concluded from the perspective of affected people and environmental indicators. The policy recommendations for disaster prevention in the context of public participation were also proposed.

Enabling the public to grasp the state and trends of emerging technologies facilitates technology adoption, socioeconomic investment, and business growth. Science journalism is crucial in connecting the scientific community with the public. This study focuses on the metaverse due to its rapid expansion. To validate knowledge extraction, we analyze relevant metaverse news from TechCrunch.com, covering 2020 to 2023, to build the domain knowledge schema. This study introduces a novel approach that combines Latent Dirichlet Allocation (LDA) topic modeling with bibliometrics as a computational intelligent method to discover topics and construct knowledge based on technology news framing. LDA is used to identify the topics in metaverse news, while a bibliometrics method, i.e., co-word networks analysis, clarifies term association strength and visualizes the findings. The study outlines the seven key elements highlighted in technology journalism, which shape public perception and expectations of new technologies. These elements offer insights into the factors influencing the development, diffusion, and adoption of new technologies. The extracted representative terms and enterprises from eight topics help map the knowledge architecture diagram for the domain. The research contributes to helping stakeholders systematically understand metaverse technology topics and demonstrate collaborative partnerships between enterprises.

Text representation is a critical issue for exploring the insights behind the text. Many models have been developed to represent the text in defined forms such as numeric vectors where it would be easy to calculate the similarity between the documents using the well-known distance measures. In this paper, we aim to build a model to represent text semantically either in one document or multiple documents using a combination of hierarchical Latent Dirichlet Allocation (hLDA), Word2vec, and Isolation Forest models. The proposed model aims to learn a vector for each document using the relationship between its words’ vectors and the hierarchy of topics generated using the hierarchical Latent Dirichlet Allocation model. Then, the isolation forest model is used to represent multiple documents in one representation as one profile to facilitate finding similar documents to the profile. The proposed text representation model outperforms the traditional text representation models when applied to represent scientific papers before performing content-based scientific papers recommendation for researchers.

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

We develop a new measure of innovation using the text of analyst reports of S&P 500 firms. Our text-based measure gives a useful description of innovation by firms with and without patenting and R&D (research and development). For nonpatenting firms, the measure identifies innovative firms that adopt novel technologies and innovative business practices (e.g., Walmart’s cross-geography logistics). For patenting firms, the text-based measure strongly correlates with valuable patents, which likely capture true innovation. The text-based measure robustly forecasts greater firm performance and growth opportunities for up to four years, and these value implications hold just as strongly for innovative nonpatenting firms. This paper was accepted by Gustavo Manso, finance.

Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges of topic modelling, which will definitely give researchers an insight for good research.

Understanding consumers' associations with brands is at the core of brand management. However, measuring associations is challenging because consumers can associate a brand with many objects, emotions, activities, sceneries, and concepts. This article presents an elicitation platform, analysis methodology, and results on consumer associations of U.S. national brands. The elicitation is direct, unaided, scalable, and quantitative and uses the power of visuals to depict a detailed representation of respondents' relationships with a brand. The proposed brand visual elicitation platform allows firms to collect online brand collages created by respondents and analyze them quantitatively to elicit brand associations. The authors use the platform to collect 4,743 collages from 1,851 respondents for 303 large U.S. brands. Using unsupervised machine-learning and image-processing approaches, they analyze the collages and obtain a detailed set of associations for each brand, including objects (e.g., animals, food, people), constructs (e.g., abstract art, horror, delicious, famous, fantasy), occupations (e.g., musician, bodybuilder, baker), nature (e.g., beach, misty, snowscape, wildlife), and institutions (e.g., corporate, army, school). The authors demonstrate the following applications for brand management: obtaining prototypical brand visuals, relating associations to brand personality and equity, identifying favorable associations per category, exploring brand uniqueness through differentiating associations, and identifying commonalities between brands across categories for potential collaborations.

This study examines analyst information intermediary roles using a textual analysis of analyst reports and corporate disclosures. We employ a topic modeling methodology from computational linguistic research to compare the thematic content of a large sample of analyst reports issued promptly after earnings conference calls with the content of the calls themselves. We show that analysts discuss exclusive topics beyond those from conference calls and interpret topics from conference calls. In addition, we find that investors place a greater value on new information in analyst reports when managers face greater incentives to withhold value-relevant information. Analyst interpretation is particularly valuable when the processing costs of conference call information increase. Finally, we document that investors react to analyst report content that simply confirms managers’ conference call discussions. Overall, our study shows that analysts play the information intermediary roles by discovering information beyond corporate disclosures and by clarifying and confirming corporate disclosures. The Internet appendix is available at https://doi.org/10.1287/mnsc.2017.2751 . This paper was accepted by Suraj Srinivasan, accounting.