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Automated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focussed on automation of visual information, such as images, when improving timely maintenance decisions. This study aims to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM).

To improve and automate the inspection-repair process, image classification algorithms were used to connect images with a corresponding image database in a BIM knowledge repository. Quick response (QR) code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system.

The system developed in this study associates the inspection-repair process with a digital three-dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with ecision-making.

This study introduces an innovative approach that applies image classification and leverages a BIM knowledge repository to enhance the inspection-repair process in FM practice. The system designed provides automated image-classifying data from a smart phone, eliminates time required to input image data manually and improves communication and collaboration between FM personnel for maintenance in the decision-making process.

The purpose of this paper is to focus on the problem of named entity disambiguation. The paper disambiguates named entities on a very detailed level. To each entity is assigned a concrete identifier of a corresponding Wikipedia article describing the entity.

For such a fine‐grained disambiguation a correct representation of the context is crucial. The authors compare various context representations: bag of words representation, linguistic representation and structured co‐occurrence representation. Models for each representation are described and evaluated. They also investigate the possibilities of multilingual named entity disambiguation.

Based on this evaluation, the structured co‐occurrence representation provides the best disambiguation results. It showed up that this method could be successfully applied also on other languages, not only on English.

Despite its good results the structured co‐occurrence context representation has several limitations. It trades precision for recall, which might not be desirable in some use cases. Also it is not able to disambiguate two different types of entities, which are mentioned under the same name in the same text. These limitations can be overcome by combination with other described methods.

The authors provide a ready‐made web service, which can be directly plugged in existing applications using a REST interface.

The paper proposes a new approach to named entity disambiguation exploiting various context representation models (bag of words, linguistic and structural representation). The authors constructed a comprehensive dataset based on all English Wikipedia articles for named entity disambiguation. They evaluated and compared the individual context representation models on this dataset. They evaluate the support of multiple languages.

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons.

Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons.

The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment.

The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences.

This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

论可推广性的情感分析法以创建酒店字典：以TripAdvisor酒店评论为样本的大数据分析

对于在线游客评论的研究在过去的几年中与日俱增, 但是仍缺乏有效方法能在有限的时间喝预算内提供终端用户价值。本论文开发并测试了一套情感分析的新方法, 创建两套酒店相关的词库, 此方法超越了标准词典式分析法。

研究样本为TripAdvisor酒店客户评论的大数据, 通过开发崭新的有配重的词库法, 来开展两极式情感分析。这个崭新的具有配重的词库法能够呈现透明化和可复制的程序, 准备、创建、并检验情感分析的词条。这个方法用到了两种词典（有配重的词典L1和手动选择的词典L2）, 本论文通过对TripAdvisor大数据进行使用词类划分精准度, 来检测和验证这两种词典。本论文采用两种热门方法（公共词典法和复杂机器学习算法）来对比词典的准确度。

精确度对比结果证实了本论文的方法, 相较于机器学习算法, 显著地超越了以字典为基础的方法。研究结果还表明, 本论文的方法可以就预测用户情感趋势进行推广。

本论文开发并验证了一项方法, 这种方法通过创建可信的词典进行大数据分析, 以判定用户情感。本论文创建的L2酒店词库对分析客人反馈是可靠有用的工具, 这个词库还能帮助酒店经理了解、预测、以及积极相应客人的态度和改变。本论文还提出了一项可以了解每个用户情感的简易方法, 这项方法可以通过对比的方式来检测和了解客人不同时间的情感变化, 以及根据其不同背景和经历的不同用户之间的变化。

本论文提出并检测了一项新方法, 这项情感分析方法可以解决之前方法的局限并立脚于旅游行业。基于文献综述, 本论文是首篇研究, 使用词库法来进行情感分析和创建特别领域词典的方式。

The paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.

The paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.

The paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.

The study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

The paper aims to estimate how corporate philanthropy expenditures and corporate philanthropy disclosure (in general and in different spheres) affect investment attractiveness of Russian companies.

To assess the degree of corporate philanthropy disclosure the authors compiled lexicons based on a set of techniques: text and frequency analysis, correlations, principal component analysis. To adjust the existing classifications of corporate philanthropic activities to the Russian market the authors employed expert analysis. The empirical research base includes 83 Russian publicly traded companies for the period 2013–2019. To estimate the impact of indicators of corporate philanthropy disclosure on company's investment attractiveness the authors utilized panel data regression and random forest algorithm.

We compiled 2 Russian lexicons: one on general issues of corporate philanthropy and another one on philanthropic activities in various spheres (sports and healthcare; support for certain groups of people; social infrastructure; children protection and youth policy; culture, education and science). 2. The paper observes that the disclosure of non-financial data including that related to general issues of corporate philanthropy as well as to different spheres affects the market capitalization of the largest Russian companies. The results of regression analysis suggest that disclosure of altruism-driven philanthropic activities (such as corporate philanthropy in the sphere of culture, education and science) has a lesser impact on company's investment attractiveness than that of activities driven by business-related motives (sports and healthcare, children protection and youth policy).

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the authors’ compiled lexicons.

The study aims to expand the domains of signaling and agency theories. First, this subject has not been widely examined in terms of emerging markets, the authors’ study is the first to focus on the Russian market. Secondly, the majority of scholars use text analysis to examine not only the impact of charitable donations but also the effect of corporate philanthropy disclosure. Thirdly, the authors provided the authors’ own lexicon of corporate philanthropy disclosure based on machine learning technique and expert analysis. Fourthly, to estimate the impact of corporate philanthropy on company's investment attractiveness the authors used the original approach based on combination of linear (regression), and non-linear methods (permutation importance. The authors’ findings extend the theoretical concept of Peterson et al. (2021): corporate philanthropy is viewed as the company strategy to reinforce its reputation, it helps to establish more efficient relationships with stakeholders which, in its turn, results in the increased business value.

Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner.

Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words.

The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach.

By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

To overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).

The authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.

Using the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.

The authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.