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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.

Examining research topics in a specific area such as accounting is important to both novice and veteran researchers. The present study aims to identify the research topics in the area of accounting and to investigate the research trends by finding hot and cold topics from all those identified ones in the field.

A new dependency-based method focusing on noun phrases, which efficiently extracts research topics from a large set of library data, was proposed. An AR(1) autoregressive model was used to identify topics that have received significantly more or less attention from the researchers. The data used in the study included a total of 4,182 abstracts published in six leading (or premier) accounting journals from 2000 to May 2019.

The study identified 48 important research topics across the examined period as well as eight hot topics and one cold topic from the 48 topics.

The research topics identified based on the dependency-based method are similar to those found with the technique of latent Dirichlet allocation latent Dirichlet allocation (LDA) topic modelling. In addition, the method seems highly efficient, and the results are easier to interpret. Last, the research topics and trends found in the study provide reference to the researchers in the area of accounting.

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.

The authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.

The authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.

First, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC.

A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words.

Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains.

Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach.

This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.

Environmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into Industry 4.0 and environmental sustainability.

This study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent semantic analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated ten clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration.

In this study, three research questions discuss the role of environmental sustainability with Industry 4.0. The author predicted ten clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources and network analysis related to the topic. Finally, the study provided industrialization’s effect on environmental sustainability and the future aspect of automation.

The reliability of the current study may be compromised, notwithstanding the size of the sample used. Poor retrieval of the literature corpus can be attributed to the limitations imposed by the search words, synonyms, string construction and variety of search engines used, as well as to the accurate exclusion of results for which the search string is insufficient.

This research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords–document relationship.

Do consumers rate reviews describing other consumers' sensory experience of a product (touch, smell, sight, hear and taste) as helpful or do they rate reviews describing more practical properties (product performance and characteristics/features) as more helpful? What is the effect of review helpfulness on purchase intention? Furthermore, why do consumers perceive sensory and non-sensory reviews differently? This study answers these questions.

The authors analyze 447,792 Amazon reviews and perform a topic modeling analysis to extract the main topics that consumers express in their reviews. Then, the topics were used as regressors to predict the number of consumers who found the review helpful. Finally, a lab experiment was conducted to replicate the results in a more controlled environment to test the serial mediation effect.

Contrary to the overwhelming evidence supporting the positive effects of sensory elicitation in marketing, this study shows that sensory reviews are less likely to be helpful than non-sensory reviews. Moreover, a key reason why sensory reviews are less effective is that they decrease the objective perception of the review, a less objective review then decreases the level of helpfulness, which decreases purchase intention.

This study contributes to the interactive marketing field by investigating customer behavior and interactivity in online shopping sites and to the sensory marketing literature by identifying a boundary condition, the authors’ data suggest that sensory elicitations might not be processed positively by consumers when they are not directly experienced, but instead communicated by another consumer. Moreover, this study indicates how companies can encourage consumers to share more effective and helpful reviews.

Previous corporate social responsibility (CSR) research has mainly revolved around the “usual target” (that is, fans and consumers) that invest money, time and energy in supporting their teams in isolation while largely ignoring individual members of the public. Building on social exchange theory and social media analytics, the authors examine the social outcomes of CSR aggregated from individual members of society's perceived benefits (intangible and psychological).

Raw data were drawn from the CSR-focused Twitter accounts of six professional leagues (i.e. @nbacares, @nflplay60, @InspireChange, @thewnbpa, @Pr_nhl, @Mlsworks and @Mlbsocial). The authors collected historical data from each CSR-focused Twitter account (N = 136,076) from March 2010 to September 2022.

After conducting sentiment analysis of public perceptions, the majority of tweets (53%) were neutral, 39% were positive and 8% were negative. All CSR-related accounts received more positive tweets about their initiatives than negative ones did. The most prevalent positive topics are supporting the community, education, youth wellness and health and inspiring the young generation. The most prevalent negative topics were related to fake, hypocrite, hate and social justice.

The study contributes to the CSR-sport literature by incorporating members of the general public into the stakeholder ecosystem and empirically examining their perceptions of sport organizations' CSR activities. Also, by drawing on the social exchange theory and the unique nature of social media, the authors highlight when and how the public expresses positive, neutral and negative perceptions over time. Finally, it joins a small but growing body of research that adopts the application of big data to sport management, and it measures the sentiment, frequency, distribution and topics of tweets, thereby determining positive and negative public perceptions.

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.

The extant literature has utilized the SERVQUAL scale to measure service quality dimensions and their importance towards customer-satisfaction using close-ended survey-based questions and not open-ended questions and/or user-generated qualitative responses. On the other hand, while measuring customer-satisfaction drivers from user-generated content (UGC), extant studies have majorly used overall or aspect-wise evaluations and not evaluations specific to SERVQUAL dimensions. In this study, the authors try to bridge the gap.

The authors suggest a methodology consisting of text mining, machine learning and econometric techniques that can measure consumer evaluations of SERVQUAL dimensions. The authors used qualitative and quantitative UGC obtained from 27,052 online reviews on 362 airlines by reviewers of 158 nationalities for our analysis.

The authors established a unique method which combines qualitative and qualitative UGC to measure service quality. The authors have also uncovered the comparative importance of such dimensions in creating customer-satisfaction and recommendation in the context of the airline industry.

The paper is one of the pioneering studies that try to find measures of SERVQUAL dimensions from online consumer reviews and their influence on customer satisfaction.

The aim of this paper is to provide a critical analysis of the main literature contributions that concern smart tourism development and management, highlighting gaps and logical inconsistencies. In addition, to further stress the importance of the issues at stake, a simulation is performed for showing how technology allows achieving better outcomes when a certain level of efficiency is obtained via re-engineering of main organizational and operational processes.

A content analysis of recent relevant literature is performed with the help of machine learning topic modelling algorithms. A network analytic approach to digital ecosystems, then, is used to study the relationship between technological tools and physical entities in a destination and how these and their combination affect the efficiency of the system at local and global levels.

The literature analyzed lacks a good discussion on the necessity to improve and rationalize the operational and organizational processes while emphasizing mostly the technological aspects. On the other hand, the simulation case presented shows that if information and knowledge flows are reasonably efficient and well organized in the physical world, the integration of digital components further enhances these processes, whereas inefficiencies can hinder the flow of information and reduce its efficiency.

Apart from the methods used, relatively little explored, the authors show that, as also much of the computer science literature states, a fundamental prerequisite for successful “smart” projects is a logical and effective restructuring of the main operational and organizational processes.

本论文旨在分析关于智慧旅游发展和管理话题的主要文献, 指出文献缺口和逻辑矛盾。此外, 为了进一步指出这个话题的重要性, 本论文运行了一个模拟程序, 以证明科技如何帮助冲破瓶颈达到新的高峰, 通过重组重要组织和流程管理。

本论文采用内容分析法, 并借助机器学习建模程序。本论文采用电子生态环境的网络分析方法来研究科技工具与实体设备在旅游目的地中的关联, 以及如何这些设备资源能够融合在区域和全球范围内提高系统效率。

文献分析结果表明, 大部分文章都着重强调科技方面, 而忽略了运营和组织流程的改进。此外, 本论文展示的模拟案件表明如果信息和知识流在实体世界中有效的利用和管理, 那么与电子软配件的结合就会更加相得益彰, 相反, 如果未达到有效结合, 那么将阻碍信息流和降低效率。

除去本文利用的研究方法相对很少学者涉及, 正如计算机科学文献所说, 本论文证实了成功的“智能”项目所需要的前提条件是主要运营和管理流程的逻辑有效的重组。