Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes

To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.

Research and Enlightenment of Text Mining Applications in ADR from Social Media

Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).

The Early Emotional Responses and Central Issues of People in the Epicenter of the COVID-19 Pandemic: An Analysis from Twitter Text Mining

This study aimed to explore citizens’emotional responses and issues of interest in the context of the coronavirus disease 2019(COVID-19)pandemic.The dataset comprised 65,313 tweets with the location marked as New York State.The data collection period was four days of tweets when New York City imposed a lockdown order due to an increase in confirmed cases.Data analysis was performed using R Studio.The emotional responses in tweets were analyzed using the Bing and NRC(National Research Council Canada)dictionaries.The tweets’central issue was identified by Text Network Analysis.When tweets were classified as either positive or negative,the negative sentiment was higher.Using the NRC dictionary,eight emotional classifications were devised:“trust,”“fear,”“anticipation,”“sadness,”“anger,”“joy,”“surprise,”and“disgust.”These results indicated that citizens showed negative and trusting emotional reactions in the early days of the pandemic.Moreover,citizens showed a strong interest in overcoming and coping with other people such as social solidarity.Citizens were concerned about the confirmation of COVID-19 infection status and death.Efforts should be made to ensure citizens’psychological stability by promptly informing them of the status of infectious disease management and the route of infection.

Environmental complaint insights through text mining based on the driver,pressure,state,impact,and response(DPSIR)framework:Evidence from an Italian environmental agency

Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.

A Novel Framework for Biomedical Text Mining

Text mining has emerged as an effective method of handling and extracting useful information from the exponentially growing biomedical literature and biomedical databases.We developed a novel biomedical text mining model implemented by a multi-agent system and distributed computing mechanism.Our distributed system,TextMed,comprises of several software agents,where each agent uses a reinforcement learning method to update the sentiment of relevant text from a particular set of research articles related to specific keywords.TextMed can also operate on different physical machines to expedite its knowledge extraction by utilizing a clustering technique.We collected the biomedical textual data from PubMed and then assigned to a multi-agent biomedical text mining system,where each agent directly communicates with each other collaboratively to determine the relevant information inside the textual data.Our experimental results indicate that TexMed parallels and distributes the learning process into individual agents and appropriately learn the sentiment score of specific keywords,and efficiently find connections in biomedical information through text mining paradigm.

Automatic Surveillance of Pandemics Using Big Data and Text Mining

COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms.

Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the results.The experimental results and evaluations show significant improvements incomparison with the available techniques.

A Parallel Platform for Web Text Mining

With user-generated content, anyone can De a content creator. This phenomenon has infinitely increased the amount of information circulated online, and it is beeoming harder to efficiently obtain required information. In this paper, we describe how natural language processing and text mining can be parallelized using Hadoop and Message Passing Interface. We propose a parallel web text mining platform that processes massive amounts data quickly and efficiently. Our web knowledge service platform is designed to collect information about the IT and telecommunications industries from the web and process this in-formation using natural language processing and data-mining techniques.

Text Mining Analysis of Efficiency of the Continuously Implemented Gathering Type Action Plan for Male Elderly People Obtained

Aim: To clarify transformation of the participants’ consciousness for rebuilding the community and its factors from the discussion contents by actions for male elderly people in Town A in Fukushima prefecture. Design: This study was an action research. Method: The author verbalized discussion contents of the action conducted in 2018-2019 and analyzed them for each year by the text mining method. Results: The word appearance frequency was high in the order of “Person” and “Town A” in both years. One large word network was formed in 2018 and its topic was about what the participants feel in their life in Town A. Two large word networks were formed in 2019 and their topic was about the community participation including difficulty in motivating others such as how people who do not participate can feel like joining it.

Drug discovery and potential gene and pathway associated with polycystic ovary syndrome through text mining and biomedical databases

Objectives:Polycystic ovary syndrome(PCOS)is a common endocrine disease in women of childbearing age.Although it is a leading cause of menstrual disorders,infertility,obesity,and other diseases,its molecular mechanism remains unclear.This study aimed to analyze the target genes,pathways,and potential drugs for PCOS through text mining.Methods:First,three different keywords("polycystic ovary syndrome","obesity/adiposis",and"anovulation")were uploaded to GenCLiP3 to obtain three different gene sets.We then chose the common genes among these gene sets.Second,we performed gene ontology and signal pathway enrichment analyses of these common genes,followed by protein-protein interaction(PPI)network analysis.Third,the most significant gene module clustered in the protein-protein network was selected to identify potential drugs for PCOS via gene-drug analysis.Results:A total of 4291 genes related to three different keywords were obtained through text mining,72 common genes were filtered among the three gene sets,and 69 genes participated in PPI network construction,of which 23 genes were clustered in the gene modules.Finally,six of the 23 genes were targeted by 30 existing drugs.Conclusions:The discovery of the six genes(CYP19A1,ESR1,IGF1R,PGR,PTGS2,and VEGFA)and 30 targeted drugs,which are associated with ovarian steroidogenesis(P<0.001),may be used in potential therapeutic strategies for PCOS.

Identifying miRNA biomarkers of polycystic ovary syndrome through text mining

Objective:Polycystic ovary syndrome(PCOS)is an endocrine disorder with diverse clinical manifestations that often occurs in women of childbearing age.However,its molecular pathogenesis remains unclear,and this study aimed to identify miRNA targets in PCOS through text mining and database analysis.Methods:First,three different sets of text mining genes(TMGs)associated with"polycystic ovary syndrome","obesity/adiposis",and"anovulation"keywords were retrieved from the GenCLiP3 database,and overlapping genes were selected.Second,Gene ontology annotation and biological pathway enrichment analyses of these overlapping TMGs were performed,followed by protein-protein interaction(PPI)network analysis.Third,genes in the gene module clustered in the PPI were selected to predict potential miRNAs for PCOS via miRNA-mRNA analysis.Results:A total of 4291 TMGs related to three different keywords were obtained through text mining;72 intersect TMGs were retained among the three gene sets,and 62 TMGs participated in the establishment of the PPI network,of which 18 were aggregated in the gene module.Finally,11 miRNAs that simultaneously bound to two TMGs(IGF1,ESR1,MAPK1,NAMPT,PIK3CA,and SERPINE1)could be prioritized as targets to study PCOS.Conclusion(s):The discovery of 11 miRNAs(miR-301a-3p,miR-301b-3p,miR-3666,miR-454-3p,miR-130a-3p,miR-130b-3p,miR-4295,miR-190a-3p,miR-5011-5p,miR-548c-3p,and miR-4799-5p)and 6 TMGs,which are associated with the HIF-1 signaling pathway(P=4.799E-08),could be used as potential targets for PCOS.

Uncovering the influence of ChatGPT's prompts on traffic safety information using text mining approach

ChatGPT has emerged as a promising advanced large language model that needs prompt to gain information.However,designing a good prompt is not an easy task for many end-users.Therefore,this study intends to determine the amount of information gained because of varied amounts of information in the prompt.This study used two types of prompts,initial and improved,to query the introduction sections of 327 highly cited articles on traffic safety.The queried introduction sections were then matched with the corresponding human-written introduction sections from the same articles.Similarity tests and text network analysis were used to understand the level of similarities and the content of ChatGPT-generated and human-written introductions.The findings indicate the improved prompts,which have the addition of generic persona and information about the citations and references,changed the ChatGPT's output insignificantly.While the perfect similar contents are supposed to have a 1.0 similarity score,the initial and improved prompt's introduction materials have average similarity scores of 0.5387 and 0.5567,respectively.Further,the content analysis revealed that themes such as statistics,trends,safety measures,and safety technologies are more likely to have high similarity scores,irrespective of the amount of information provided in the prompt.On the other hand,themes such as human behavior,policy and regulations,public perception,and emerging technologies require a detailed level of information in their prompt to produce materials that are close to human-written materials.The prompt engineers can use the findings to evaluate their outputs and improve their prompting skills.

Text Mining of Rheumatoid Arthritis and Diabetes Mellitus to Understand the Mechanisms of Chinese Medicine in Different Diseases with Same Treatment

Objective： To identify the commonalities between rheumatoid arthritis （RA） and diabetes mellitus （DM） to understand the mechanisms of Chinese medicine （CM） in different diseases with the same treatment. Methods： A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identified via text mining was used as the intervention. Results： Similarities were identified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM. Conclusion： Commonalities between the biological activities involved in RA and DM were identified through text mining, and both RA and DM might be responsive to the same intervention at a specific stage.

Short Text Mining Framework with Specific Design for Operation and Maintenance of Power Equipment

In order to recover the value of short texts in the operation and maintenance of power equipment,a short text mining framework with specific design is proposed.First,the process of the short text mining framework is summarized,in which the functions of all the processing modules are introduced.Then,according to the characteristics of short texts in the operation and maintenance of power equipment,the specific design for each module is proposed,which adapts the short text mining framework to a practical application.Finally,based on the framework with the specific designed modules,two examples in terms of defect texts are given to illustrate the application of short text mining in the operation and maintenance of power equipment.The results of the examples show that the short text mining framework is suitable for operation and maintenance tasks for power equipment,and the specific design for each module is beneficial for the improvement of the application effect.

Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology

Defect factors and their relevant rules can be analyzed in depth by processing defect records which are often expressed in the form of text data.However,considering that defect text consists of both structured and unstructured data,it is necessary to excavate structured information from unstructured data.In this paper,a text mining method based on semantic framework technology is introduced to transform unstructured defect description into structured information such as components and defect attributes.Then,a deep analyzing model of a power equipment defect is established,which provides a scheme of defect mining based on historical defect texts.Case studies prove that the proposed deep analysis method has a guiding significance for equipment upgrading,selection and maintenance.

Comprehensive review of text‑mining applications in finance

Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance.Therefore,reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research.This paper focuses on the text-mining literature related to financial forecasting,banking,and corporate finance.It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies.Finally,the paper briefly discusses various text-mining methods being applied in the financial domain,the challenges faced in these applications,and the future scope of text mining in finance.

Semantic Relation Annotation for Biomedical Text Mining Based on Recursive Directed Graph

In this paper we propose a novel model ＂recursive directed graph＂ based on feature structure, and apply it to represent the semantic relations of postpositive attributive structures in biomedical texts. The usages of postpositive attributive are complex and variable, especially three categories： present participle phrase, past participle phrase, and preposition phrase as postpositire attributive, which always bring the difficulties of automatic parsing. We summarize these categories and annotate the semantic information. Compared with dependency structure, feature structure, being recursive directed graph, enhances semantic information extraction in biomedical field. The annotation results show that recursive directed graph is more suitable to extract complex semantic relations for biomedical text mining.

Geographic context-aware text mining:enhance social media message classification for situational awareness by integrating spatial and temporal features

To find disaster relevant social media messages,current approaches utilize natural language processing methods or machine learning algorithms relying on text only,which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when posted.Meanwhile,a disaster relevant social media message is highly sensitive to its posting location and time.However,limited studies exist to explore what spatial features and the extent of how temporal,and especially spatial features can aid text classification.This paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets,along with the text information,for classifying disaster relevant social media posts.This work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data,and then used to enhance text mining.The deep learning-based method and commonly used machine learning algorithms,assessed the accuracy of the enhanced text-mining method.The performance results of different classification models generated by various combinations of textual,spatial,and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification.

Hybrid Reliability Parameter Selection Method Based on Text Mining, Frequent Pattern Growth Algorithm and Fuzzy Bayesian Network

Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data firstly, and frequent pattern tree(FPT) of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth(FPG) algorithm. Then on the basis of fuzzy Bayesian network(FBN)and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective.

Text Mining and Analysis of Treatise on Febrile Diseases Based on Natural Language Processing

Objective:With using natural language processing (NLP) technology to analyze and process the text of "Treatise on Febrile Diseases (TFDs)"for the sake of finding important information, this paper attempts to apply NLP in the field of text mining of traditional Chinese medicine (TCM)literature. Materials and Methods:Based on the Python language, the experiment invoked the NLP toolkit such as Jieba, nltk, gensim,and sklearn library, and combined with Excel and Word software. The text of "TFDs" was sequentially cleaned, segmented, and moved the stopped words, and then implementing word frequency statistics and analysis, keyword extraction, named entity recognition (NER) and other operations, finally calculating text similarity. Results:Jieba can accurately identify the herbal name in "TFDs." Word frequency statistics based on the word segmentation found that "warm therapy" is an important treatment of "TFDs." Guizhi decoction is the main prescription,and five core decoctions are identified. Keyword extraction based on the term "frequency-inverse document frequency" algorithm is ideal.The accuracy of NER in "TFDs" is about 86%;latent semantic indexing model calculating the similarity,"Understanding of Synopsis of Golden Chamber (SGC)" is much more similar with "SGC" than with "TFDs." The results meet expectation. Conclusions:It lays a research foundation for applying NLP to the field of text mining of unstructured TCM literature. With the combination of deep learning technology,NLP as an important branch of artificial intelligence will have broader application prospective in the field of text mining in TCM literature and construction of TCM knowledge graph as well as TCM knowledge services.