From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach.

INTRODUCTION: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts' subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. METHOD: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. CONCLUSIONS AND PRACTICAL APPLICATIONS: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Analysis of Critical Incident Reports Using Natural Language Processing.

A Critical Incident Reporting System (CIRS) collects anecdotal reports from employees, which serve as a vital source of information about incidents that could potentially harm patients. OBJECTIVES: To demonstrate how natural language processing (NLP) methods can help in retrieving valuable information from such incident data. METHODS: We analyzed frequently occurring terms and sentiments as well as topics in data from the Swiss National CIRRNET database from 2006 to 2023 using NLP and BERTopic modelling. RESULTS: We grouped the topics into 10 major themes out of which 6 are related to medication. Overall, they reflect the global trends in adverse events in healthcare (surgical errors, venous thromboembolism, falls). Additionally, we identified errors related to blood testing, COVID-19, handling patients with diabetes and pediatrics. 40-50% of the messages are written in a neutral tone, 30-40% in a negative tone. CONCLUSION: The analysis of CIRS messages using text analysis tools helped in getting insights into common sources of critical incidents in Swiss healthcare institutions. In future work, we want to study more closely the relations, for example between sentiment and topics.

Advancing the allergenicity assessment of new proteins using a text mining resource.

With a society increasingly demanding alternative protein food sources, new strategies for evaluating protein safety issues, such as allergenic potential, are needed. Large-scale and systemic studies on allergenic proteins are hindered by the limited and non-harmonized clinical information available for these substances in dedicated databases. A missing key information is that representing the symptomatology of the allergens, especially given in terms of standard vocabularies, that would allow connecting with other biomedical resources to carry out different studies related to human health. In this work, we have generated the first resource with a comprehensive annotation of allergens' symptomatology, using a text-mining approach that extracts significant co-mentions between these entities from the scientific literature (PubMed, ∼36 million abstracts). The method identifies statistically significant co-mentions between the textual descriptions of the two types of entities in the literature as indication of relationship. 1,180 clinical signs extracted from the Human Phenotype Ontology, the Medical Subject Heading terms of PubMed together with other allergen-specific symptoms, were linked to 1,036 unique allergens annotated in two main allergen-related public databases via 14,009 relationships. This novel resource, publicly available through an interactive web interface, could serve as a starting point for future manually curated compilation of allergen symptomatology.

Student loan debt and mental health: a comprehensive review of scholarly literature from 1900 to 2019.

PURPOSE: The review had two purposes. The first was to examine the nature and extent of published literature on student loan and the second was to systematically review the literature on student loans and mental health. MATERIALS AND METHODS: Data from academic databases (1900-2019) were analyzed using two methods. First, topic modeling (a text-mining tool that utilized Bayesian statistics to extract hidden patterns in large volumes of texts) was used to understand the topical coverage in peer-reviewed abstracts (n = 988) on student debt. Second, using PRISMA guidelines, 46 manuscripts were systematically reviewed to synthesize literature linking student debt and mental health. RESULTS: A model with 10 topics was selected for parsimony and more accurate clustered representation of the patterns. Certain topics have received less attention, including mental health and wellbeing. In the systematic review, themes derived were categorized into two life trajectories: before and during repayment. Whereas stress, anxiety, and depression dominated the literature, the review demonstrated that the consequences of student loans extend beyond mental health and negatively affect a person's wellbeing. Self-efficacy emerged as a potential solution. DISCUSSION AND CONCLUSION: Across countries and samples, the results are uniform and show that student loan burdens certain vulnerable groups more. Findings indicate diversity in mental health measures has resulted into a lack of a unified theoretical framework. Better scales and consensus on commonly used terms will strengthen the literature. Some areas, such as impact of student loans on graduate students or consumers repaying their loans, warrant attention in future research.

A semiautomated risk assessment method for consumer products.

In this study, we develop a model that assesses product risk using online reviews from Amazon.com. We first identify unique words and phrases capable of identifying hazards. Second, we estimate risk severity using hazard type weights and risk likelihood using total reviews as a proxy for sales volume. In addition, we obtain expert assessments of product hazard risk (risk likelihood and severity) from a sample of high- and low-risk consumer products identified by a computerized risk assessment model we have developed. Third, we assess the validity of our computerized product risk assessment scoring model by utilizing the experts' survey responses. We find that our model is especially consistent with expert judgments of hazard likelihood but not as consistent with expert judgments of hazard severity. This model helps organizations to determine the risk severity, risk likelihood, and overall risk level of a specific product. The model produced by this study is helpful for product safety practitioners in product risk identification, characterization, and mitigation.

Big data sentiment analysis of business environment public perception based on LTP text classification --Take Heilongjiang province as an example.

At present, the research of business environment is limited to conducting surveys on specific groups or measuring data from official databases. The assessment of the business environment largely depends on public perception. Aiming to explore the public perception of business environment, this paper organically combines the big data text mining and sentiment analysis (SA). The results show that the combination of big data text mining and SA can reflect the theme characteristics, reduce the bias of sentiment and text analysis, and clearly show the public perception of the business environment. The empirical study found that the public perception of business environment depends on not only the four dimensions of business environment, but also the influence of public opinion that cannot be ignored. The public's low recognition of the business environment in Heilongjiang Province mainly includes backward economic development, serious brain drain, low government efficiency, imperfect policies, administrative law enforcement, regional climate and urban construction. In order to solve these problems, it is necessary to improve the high-standard market system to promote economic development, enhance the efficiency of government services, improve government policies, effectively enhance law enforcement, strengthen infrastructure construction and promote cultural innovation.

Understanding unmet medical needs through medical crowdfunding in China.

OBJECTIVES: Online medical crowdfunding has gained popularity in recent years in China. The objective of this study was to identify unmet medical needs in the public healthcare system through analysis of Chinese medical crowdfunding data. STUDY DESIGN: Text information extraction and statistical analysis based on large-scale data. METHODS: From 19 June 2011 to 15 March 2020, data from 30,704 medical crowdfunding projects were collected from Tencent GongYi, which is one of the largest Chinese medical crowdfunding platforms. Text mining methods were used to extract data on the medical conditions and locations of the applicants of medical crowdfunding. In addition, 125 medical crowdfunding projects initiated by leukaemia patients in Chongqing and Nanyang were further investigated through manual data extraction, and the factors impacting the fundraising goals were explored using a generalised linear model. RESULTS: The most common conditions using medical crowdfunding to raise funds were as follows: cancer (31.87%), chronic conditions (18.14%), accidental injury (7.80%) and blood system-related conditions (7.75%). Treatments for cancer and blood system-related conditions are expensive and have serious long-term impacts on the lives of patients. Results showed that the cities of Nanyang and Chongqing had the largest number of crowdfunding projects. CONCLUSIONS: This study found that the medical conditions that prompted individuals to apply for crowdfunding were those with long treatment cycles, complexities and expensive medical or non-medical costs. Furthermore, discrepancies in health insurance policies between different regions and residents seeking treatments outside their insurance locations were also important factors that triggered medical crowdfunding applications. Adjusting health insurance policies accordingly may improve the efficiency of utilising health insurance resources and reduce the financial burden on patients.

A Study on Online Health Community Users' Information Demands Based on the BERT-LDA Model.

As the economy and society develop and the standard of living improves, people's health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT's semantic comprehension with LDA's topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users' emotional reactions and presents valuable insights for delivering personalized health information in online communities.

Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter.

PURPOSE: The primary objective of this study was to identify patterns in users' naturalistic expressions on student loans on two social media platforms. The secondary objective was to examine how these patterns, sentiments, and emotions associated with student loans differ in user posts indicating mental illness. MATERIAL AND METHOD: Data for this study were collected from Reddit and Twitter (2009-2020, n = 85,664) using certain key terms of student loans along with first-person pronouns as a triangulating measure of posts by individuals. Unsupervised and supervised machine learning models were used to analyze the text data. RESULTS: Results suggested 50 topics in reddit finance and 40 each in reddit mental health communities and Twitter. Statistically significant associations were found between mental illness statuses and sentiments and emotions. Posts expressing mental illness showed more negative sentiments and were more likely to express sadness and fear. DISCUSSION AND CONCLUSION: Patterns in social media discussions indicate both academic and non-academic consequences of having student debt, including users' desire to know more about their debts. Interventions should address the skill and information gaps between what is desired by the borrowers and what is offered to them in understanding and managing their debts. Cognitive burden created by student debts manifest itself on social media and can be used as an important marker to develop a nuanced understanding of people's expressions on a variety of socioeconomic issues. Higher volumes of negative sentiments and emotions of sadness, fear, and anger warrant immediate attention of policymakers and practitioners to reduce the cognitive burden of student debts.

Using text mining and multilevel association rules to process and analyze incident reports in China.

Incident investigation reports provide information on defects related to the system safety and indications for improvements. Currently, the analysis of these reports relies heavily on expert' experience. The foreseeable work-load and lack of understanding about the importance of near misses have created a situation where severe accidents are rigorously investigated, and minor incidents are often omitted. Consequently, incident reports have not been fully analyzed to provide sufficient solutions. The aim of this research is to propose a framework that uses text mining and multilevel association rules to efficiently structure Chinese incident reports and identify important incident patterns, providing an analysis of trends, rectification strategies, and guidance for safety management. A case study of a construction company in China was conducted using two years of incident data dated 2018-2019, including accidents and near misses. To identify incident elements, a pattern extraction workflow involving TextRank, and domain pertinence was devised based on the linguistic and writing styles of Chinese reports. A concept hierarchy was applied to determine the taxonomic relationships within the risk factors. Multilevel association rule mining was adopted and proven to deliver more comprehensive pattern indications. Comparative and cross-analysis of patterns in different time periods revealed the severity and temporal features of incidents as well as the effectiveness of preventive and precautionary measures. The results also highlight the importance of learning from near miss events. Decision makers can formulate countermeasures and management policies based on these results to improve safety performance.

What Do MBA Program in Southeast Asia Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder.

To analyze the directions for future research suggested and to project future research plans, we extract relevant text from these publications with respect to COVID-19-related research based on 54,136 relevant academic journals published from the initial outbreak of COVID-19 in January 2020 until December 2020. First, we extract and preprocess the corpus and then determine that, according to the Elbow method, the optimal number of clusters is 7. Then, we construct a text clustering model based on an autoencoder, with the support of an artificial neural network. Distance measurements, such as correlation, cosine, Braycurtis, and Jaccard are compared, and the clustering results are evaluated with normal mutual information. The results show that cosine similarity has the best effect on clustering of COVID-19-related documents. A topic model analysis shows that the directions of future research can mainly be grouped into the following seven categories: infectivity testing, genome analysis, vaccine testing, diagnosis and infection characteristics, pandemic management, nursing care, and clinical testing. Among them, the topics of pandemic management, diagnosis and infection characteristics, and clinical testing trended upward in proportion to future directions. The topic of vaccine testing remains steady over the observation window, whereas other topics (infectivity testing, genome analysis, and nursing care) slowly trended downward. Among all the topics, medical research comprises 80%, and about 20% of the topics are related to public management, government functions, and economic development. This study enriches our scientific understanding of COVID-19 and helps us to effectively predict future scientific research output on COVID-19.

Text Analysis of Trends in Health Equity and Disparities From the Internal Revenue Service Tax Documentation Submitted by US Nonprofit Hospitals Between 2010 and 2019: Exploratory Study.

BACKGROUND: Many US hospitals are classified as nonprofits and receive tax-exempt status partially in exchange for providing benefits to the community. Proof of compliance is collected with the Schedule H form submitted as part of the annual Internal Revenue Service Form 990 (F990H), including a free-response text section that is known for being ambiguous and difficult to audit. This research is among the first to use natural language processing approaches to evaluate this text section with a focus on health equity and disparities. OBJECTIVE: This study aims to determine the extent to which the free-response text in F990H reveals how nonprofit hospitals address health equity and disparities, including alignment with public priorities. METHODS: We used free-response text submitted by hospital reporting entities in Part V and VI of the Internal Revenue Service Form 990 Schedule H between 2010 and 2019. We identified 29 main themes connected to health equity and disparities, and 152 related key phrases. We tallied occurrences of these phrases through term frequency analysis, calculated the Moran I statistic to assess geographic variation in 2018, analyzed Google Trends use for the same terms during the same period, and used semantic search with Sentence-BERT in Python to understand contextual use. RESULTS: We found increased use from 2010 to 2019 across all the 29 phrase themes related to health equity and disparities. More than 90% of hospital reporting entities used terms in 2018 and 2019 related to affordability (2018: 2117/2131, 99.34%; 2019: 1620/1627, 99.57%), government organizations (2018: 2053/2131, 96.33%; 2019: 1577/1627, 96.93%), mental health (2018: 1937/2131, 90.9%; 2019: 1517/1627, 93.24%), and data collection (2018: 1947/2131, 91.37%; 2019: 1502/1627, 92.32%). The themes with the largest relative increase were LGBTQ (lesbian, gay, bisexual, transgender, and queer; 1676%; 2010: 12/2328, 0.51%; 2019: 149/1627, 9.16%) and social determinants of health (958%; 2010: 68/2328, 2.92%; 2019: 503/1627, 30.92%). Terms related to homelessness varied geographically from 2010 to 2018, and terms related to equity, health IT, immigration, LGBTQ, oral health, rural, social determinants of health, and substance use showed statistically significant (P<.05) geographic variation in 2018. The largest percentage point increase was for terms related to substance use (2010: 403/2328, 17.31%; 2019: 1149/1627, 70.62%). However, use in themes such as LGBTQ, disability, oral health, and race and ethnicity ranked lower than public interest in these topics, and some increased mentions of themes were to explicitly say that no action was taken. CONCLUSIONS: Hospital reporting entities demonstrate an increasing awareness of health equity and disparities in community benefit tax documentation, but these do not necessarily correspond with general population interests or additional action. We propose further investigation of alignment with community health needs assessments and make suggestions for improvements to F990H reporting requirements.

A systematic review of natural language processing applications for hydrometeorological hazards assessment.

Natural language processing (NLP) is a promising tool for collecting data that are usually hard to obtain during extreme weather, like community response and infrastructure performance. Patterns and trends in abundant data sources such as weather reports, news articles, and social media may provide insights into potential impacts and early warnings of impending disasters. This paper reviews the peer-reviewed studies (journals and conference proceedings) that used NLP to assess extreme weather events, focusing on heavy rainfall events. The methodology searches four databases (ScienceDirect, Web of Science, Scopus, and IEEE Xplore) for articles published in English before June 2022. The preferred reporting items for systematic reviews and meta-analysis reviews and meta-analysis guidelines were followed to select and refine the search. The method led to the identification of thirty-five studies. In this study, hurricanes, typhoons, and flooding were considered. NLP models were implemented in information extraction, topic modeling, clustering, and classification. The findings show that NLP remains underutilized in studying extreme weather events. The review demonstrated that NLP could potentially improve the usefulness of social media platforms, newspapers, and other data sources that could improve weather event assessment. In addition, NLP could generate new information that should complement data from ground-based sensors, reducing monitoring costs. Key outcomes of NLP use include improved accuracy, increased public safety, improved data collection, and enhanced decision-making are identified in the study. On the other hand, researchers must overcome data inadequacy, inaccessibility, nonrepresentative and immature NLP approaches, and computing skill requirements to use NLP properly.

Textual data transformations using natural language processing for risk assessment.

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause-effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

A critical analysis on the triple bottom line of sustainable manufacturing: key findings and implications.

Because of the environmental consequences of manufacturing activities, the general public, industry, and academia are becoming more aware of sustainable manufacturing (SM), which incorporates environmentally friendly manufacturing processes while emphasizing overall triple bottom line (TBL) performance in manufacturing. This article employs various text mining techniques and bibliometric analysis including cluster analysis, Pearson coefficient and research landscape to conduct an extensive investigation on SM with a focus on the TBL, in which the research content of SM with the TBL is reviewed and discussed systematically from a wide angle and with reduced bias. In this study, three new indicators about the ratios of the number of scientific papers between social, environmental, and economic dimensions of SM are devised to show the weight and level of importance of dimensions in SM, covering scientific papers from 30 years. The findings from this study indicate that the influential power of SM varies across the three dimensions, with a particular emphasis on the social dimension of SM from various countries, implying a current state of imbalance status in TBL for SM, at the same time, the economic and environmental dimensions share similar research topics and academic emphasis in SM. Based on these findings, recommendations based on sustainable development goals (SDGs) of the United Nations (UN) are made to increase the social influence of SM. This article firstly reveals the individual status of the social dimension and the situation of unbalanced TBL in SM, providing sustainable suggestions for enhancing the effectiveness of SM and achieving balanced TBL regarding the SDGs.

Data-driven evaluation framework for the effectiveness of rural vitalization in China: an empirical case study of Hubei Province.

Rural vitalization (RV) has attracted more and more attention in China, especially since the Rural Vitalization Strategy (RVS) was proposed to restrict rural decline in 2017. The evaluation of RV is an effective means to objectively identify the characteristics and problems of rural development, so exploring scientific and rational evaluation methods is important for sustainable rural development. Therefore, this study builds a data-driven evaluation framework from a "bottom-up" perspective, and selects Hubei Province as the object to evaluate the effectiveness of RV. The evaluation index system is formed based on the concept and connotation of RV, which contains six dimensions, namely thriving businesses (TB), pleasant living environments (PLE), social etiquette and civility (SEC), effective governance (EG), living in prosperity (LP), and organization system (OS). The empirical results indicate that there is a low level of variation of the total scores but an obvious disparity in the dimensional scores in 13 prefecture-level and 83 county-level regions. At county-level, the regional development stage has an impact on the effectiveness of RV, and regions with a higher economy or endowed with better resources perform better. The results of spatial analysis further reveal that there is regional agglomeration as well as differences in various dimensions, and regions with characteristic industries or policy support perform better. Compared with the traditional evaluation method, differentiated evaluation objectives and diversified data are considered in the evaluation process of this study. The results and discussion shown in this study could provide empirical evidence for policymakers to effectively promote RV in the future.

Evaluating health policies with subnational disparities: a text-mining analysis of the Urban Employee Basic Medical Insurance Scheme in China.

Subnational disparities in most health systems often defy 'one-size-fits-all' approach in policy implementation. When local authorities implement a national policy in a decentralized context, they behave as a strategic policy actor in specifying the central mandates, selecting appropriate tools and setting key implementation parameters. Local policy discretion leads to diverse policy mixes across regions, thus complicating evidence-based evaluations of policy impacts. When measuring complex policy reforms, mainstream policy evaluation methodologies have tended to adopt simplified policy proxies that often disguise distinct policy choices across localities, leaving the heterogeneous effects of the same generic policy largely unknown. Using the emerging 'text-as-data' methodology and drawing from subnational policy documents, this study developed a novel approach to policy measurement through analysing policy big data. We applied this approach to examine the impacts of China's Urban Employee Basic Medical Insurance (UEBMI) on individuals' out-of-pocket (OOP) spending. We found substantial disparities in policy choices across prefectures when categorizing the UEBMI policy framework into benefit-expansion and cost-containment reforms. Overall, the UEBMI policies lowered enrollees' OOP spending in prefectures that embraced both benefit-expansion and cost-containment reforms. In contrast, the policies produced ill effects on OOP spending of UEBMI enrollees and uninsured workers in prefectures that carried out only benefit-expansion or cost-containment reforms. The micro-level impacts of UEBMI enrolment on OOP spending were conditional on whether prefectural benefit-expansion and cost-containment reforms were undertaken in concert. Only in prefectures that promulgated both types of reforms did UEBMI enrolment reduce OOP spending. These findings contribute to a comprehensive text-mining measurement approach to locally diverse policy efforts and an integration of macro-level policy analysis and micro-level individual analysis. Contextualizing policy measurements would improve the methodological rigour of health policy evaluations. This paper concludes with implications for health policymakers in China and beyond.

Understanding information needs during COVID-19: A comparison study between an online health community and a Q&A platform.

This paper aims at identifying user's information needs on Coronavirus and the differences of user's information needs between the online health community MedHelp and the question-and-answer forum Quora during the COVID-19 global pandemic. We obtained the posts in the sub-community Coronavirus on MedHelp (195 posts with 1627 answers) and under the topic of COVID-19(2019-2020) on Quora (263 posts with 8401 answers) via web scraping built on Selenium WebDriver. After preprocessing, we conducted topic modeling on both corpora and identified the best topic model for each corpus based on the diagnostic metrics. Leveraging the improved sqrt-cosine similarity measurement, we further compared the topic similarity between these two corpora. This study finds that there are common information needs on both platforms about vaccination and the essential elements of the disease including the onset symptoms, transmission routes, preventive measures, treatment and control of COVID-19. Some unique discussions on MedHelp are about psychological health, and therapeutic management of patients. Users on Quora have special interests of information about the association between vaccine and Luciferase, and attacks on Fauci after email trove released. The work is beneficial for researchers who aim to provide accurate information assistance and build effective online emergence response programs during the pandemic.

On the economic impacts of COVID-19: A text mining literature analysis.

The COVID-19 outbreak has affected everyday lives worldwide. As governments started to implement confinement and business closure measures, the economic impact was felt by entire societies immediately. The urgency of such a theme has led researchers to study the phenomenon. Accordingly, the purpose of this research is to provide the state of the art on relevant dimensions and hot topics of research to understand the economic impacts of COVID-19. In this survey, we conduct a text mining analysis of 301 articles published during 2020 which analyzed such economic impacts. By defining a set of relevant dimensions grounded on existing literature, we were able to extract a set of coherent topics that aggregate the collected articles, characterized by the predominance of a few sets of dimensions. We found that the impact on "financial markets" was widely studied, especially in relation to Asia. Next, we found a more diverse range of themes analyzed in Europe, from "government measures" to "macroeconomic variables." We also discovered that America has not received the same degree of attention, and "institutions," "Africa," or "other pandemics" were studied less. We anticipate that future research will proliferate focusing on several themes, from environmental issues to the effectiveness of government measures.

News Text Mining-Based Business Sentiment Analysis and Its Significance in Economy.

The purpose of business sentiment analysis is to determine the emotions or attitudes expressed toward the company, products, services, personnel, or events. Text analysis are the simplest and most developed types of sentiment analysis so far. The text-based business sentiment analysis still has some unresolved challenges. For example, the machine learning algorithms are unable to recognize double meanings, jokes and allusions. The regional differences between language and non-native speech structures cannot be explained. To solve this problem, an undirected weighted graph is constructed for news topics. The sentences in an article are modeled as nodes, and the normalized sentence similarity is used as the link of the nodes, which can help avoid the influence of sentence length on the summary results. In the topic extraction process, the keywords are not limited to a single word, to achieve the purpose of improving the readability of the abstract. To improve the accuracy of sentiment classification, this work proposes a robust news mining-based business sentiment analysis framework, called BuSeD. It contains two main stages: (1) news collection and preprocessing, and (2) feature extraction and sentiment classification. In the first stage, the news is collected by using crawler tools. The news dataset is then preprocessed by reducing noises. In the second stage, topics in each article is extracted by using traditional topic extraction tools. And then a convolutional neural network (CNN)-based text analyzing model is designed to analyze news from sentence level. We conduct comprehensive experiments to evaluate the performance of BuSeD for sentiment classification. Compared with four classical classification algorithms, the proposed CNN-based classification model of BuSeD achieves the highest F1 scores. We also present a quantitative trading application based on sentiment analysis to validate BuSeD, which indicates that the news-based business sentiment analysis has high economic application value.