Text Mining Techniques Research Articles

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Online consumer reviews in e-commerce are one technique to gather consumer opinion and sentiment about a company's products and services. However, manual analysis is impractical due to natural language text's enormous volume and complexity. Text mining and sentiment analysis methods based on machine learning provide an opportunity to analyze data for marketing objectives by increasing sales, positive electronic word-of-mouth (e-WOM), and meeting consumer demands and wants through the enhancement of market offerings. Despite the numerous benefits of analyzing e-commerce reviews to assist a company's marketing strategy, very little research has focused on sentiment and acceptance for Malaysia’s local agriculture products due to mixed language (English-Malay language) processing challenges. This concept paper highlights the use of text mining techniques to extract valuable insights from e-commerce comments related to Malaysian local agriculture products. By leveraging text mining, the study aims to better understand consumer sentiments, preferences, and feedback regarding local products, thereby facilitating improved market analysis and decision-making processes.

Research on the structure function recognition of PLOS.

The present study explores and investigates the efficiency of deep learning models in identifying discourse structure and functional features and explores the potential application of natural language processing (NLP) techniques in text mining, information measurement, and scientific communication. The PLOS literature series has been utilized to obtain full-text data, and four deep learning models, including BERT, RoBERTa, SciBERT, and SsciBERT, have been employed for structure-function recognition. The experimental findings reveal that the SciBERT model performs outstandingly, surpassing the other models, with an F1 score. Additionally, the performance of different paragraph structures has been analyzed, and it has been found that the model performs well in paragraphs such as method and result. The study's outcomes suggest that deep learning models can recognize the structure and functional elements at the discourse level, particularly for scientific literature, where the SciBERT model performs remarkably. Moreover, the NLP techniques have extensive prospects in various fields, including text mining, information measurement, and scientific communication. By automatically parsing and identifying structural and functional information in text, the efficiency of literature management and retrieval can be improved, thereby expediting scientific research progress. Therefore, deep learning and NLP technologies hold significant value in scientific research.

Modest performance of text mining to extract health outcomes may be almost sufficient for high-quality prognostic model development

Background:Across medicine, prognostic models are used to estimate patient risk of certain future health outcomes (e.g., cardiovascular or mortality risk). To develop (or train) prognostic models, historic patient-level training data is needed containing both the predictive factors (i.e., features) and the relevant health outcomes (i.e., labels). Sometimes, when the health outcomes are not recorded in structured data, these are first extracted from textual notes using text mining techniques. Because there exist many studies utilizing text mining to obtain outcome data for prognostic model development, our aim is to study the impact of the text mining quality on downstream prognostic model performance. Methods:We conducted a simulation study charting the relationship between text mining quality and prognostic model performance using an illustrative case study about in-hospital mortality prediction in intensive care unit patients. We repeatedly developed and evaluated a prognostic model for in-hospital mortality, using outcome data extracted by multiple text mining models of varying quality. Results:Interestingly, we found in our case study that a relatively low-quality text mining model (F1 score ≈ 0.50) could already be used to train a prognostic model with quite good discrimination (area under the receiver operating characteristic curve of around 0.80). The calibration of the risks estimated by the prognostic model seemed unreliable across the majority of settings, even when text mining models were of relatively high quality (F1 ≈ 0.80). Discussion:Developing prognostic models on text-extracted outcomes using imperfect text mining models seems promising. However, it is likely that prognostic models developed using this approach may not produce well-calibrated risk estimates, and require recalibration in (possibly a smaller amount of) manually extracted outcome data.

Predictive modeling for technology convergence: A patent data-driven approach through technology topic networks

Anticipating technology convergence is crucial for driving innovation and gaining a competitive advantage. Developing a method for forecasting the convergence of previously unrelated technology fields is necessary. Previous studies explored technology convergence using patents but focused on technology classes. However, these prior studies lacked clear insights because they relied on ambiguous technology classes. Therefore, this study seeks to define technology topics with precise technological meanings and develop model predictions using machine learning through technology topic networks to anticipate technology convergence potential. Specifically, it explores the use of text mining techniques to gain insights into technology topics, highlighting best practices and our research findings. This study constructs a network of technology topics derived from patent documents and maps their interactions using a topic network approach. Technology topic networks are constructed through dynamic topic modeling, and features extracted from these networks are used as input for training multiple classification models. By incorporating new link results and leveraging technological influence, the proposed approach can accurately anticipate technology convergence. The results of this research can have significant implications for firms seeking to gain a competitive advantage through innovation and technology convergence and contribute to the establishment of a next-generation R&D planning system.

Customer satisfaction analysis with Saudi Arabia mobile banking apps: a hybrid approach using text mining and predictive learning techniques

Customer satisfaction analysis with Saudi Arabia mobile banking apps: a hybrid approach using text mining and predictive learning techniques

The role of tweets in agricultural export: an approach from text-mining and time-series analyses

PurposeThis study aims to investigate the relationship between consumer awareness of Zespri International Limited (Zespri™) and its sales promotion in Japan and the recent expansion of New Zealand (NZ) kiwifruit imported into Japan.Design/methodology/approachTweets mentioning Zespri™ were utilised as a proxy of such awareness. They were first summarised using two text-mining techniques: tf-idf scoring and a co-occurrence network graph. Afterwards, the authors estimated a tri-variate vector autoregression (VAR) model consisting of the net imports of NZ kiwifruit in Japan, unit import price and number of tweets. Additionally, the occurrence frequency of tweets with “Kiwi Brothers”, promotional characters for Zespri™’s sales, was added to the model, and a tetra-variate VAR model was estimated. Finally, Granger-causality tests, an estimation of the impulse response function and forecast error variance decomposition was conducted.FindingsAll these variables were found to Granger-cause each other. Furthermore, a shock in the document frequency of “Kiwi Brothers” significantly affected Japan’s kiwifruit imports from NZ, explaining approximately 20% of future imports. Zespri™’s distinctive sales promotion was, thus, found to contribute in part to the recent increase in NZ’s kiwifruit export to Japan.Originality/valueThis paper is the first to apply text-regression methodology to food consumption research; it contributes to food consumption research by proposing a practical way to combine tweets with outcome variables using a time-series analysis.

Customer satisfaction in the pet food subscription-based online services

Pet food subscription-based online services (SOS) fulfill the demand for pet food that fits pets’ characteristics and health necessities. The present research explores the antecedents of pet food SOS customer satisfaction and its effect on continuance intention, positively moderated by price. 28,786 online reviews from 10 pet food SOS brands were collected from Trustpilot to generate a term-frequency matrix through text mining techniques and used as an input to construct a structural equation model. Results suggest that e-service quality (E-SQ), perceived healthfulness, ingredients and nutritional composition, and packaging positively influence customer satisfaction, subsequently predicting continuance intention. In turn, price was not confirmed as a positive moderating factor in the relationship between customer satisfaction and continuance intention.

A survey on the role of artificial intelligence in managing Long COVID.

In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

Uncovering the dark side of the sharing economy from a provider’s perspective: a bibliometric systematic review

PurposeDespite a growing body of research focusing on the dark side of sharing economy development, arguments are fragmented and incomplete. This study aims to address the gap by integrating existing viewpoints based on a provider’s perspective.Design/methodology/approachThis study conducted a bibliometric analysis using text mining and clustering algorithm techniques to measure the scope of scientific output on this topic and identify the main research themes.FindingsThrough the bibliometric analysis, this study developed an integrative framework based on the platform providers’ internal management issues and external conflicts with consumers, society, government regulations and traditional business. It also identified significant gaps within each research theme and proposed a future research agenda.Originality/valueSharing economy development has not yet been fully understood and regulated, leading to unprecedented challenges to existing business systems. The study addresses knowledge gaps and advances the understanding of the dark side of the sharing economy based on the provider’s internal management and interplay with external forces. It offers a roadmap for future research to advance understanding of the “hidden” dark side of the sharing economy.

Towards digital approach for managing request for information (RFI) in construction projects: a literature review

Purpose Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks. Design/methodology/approach This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge. Findings Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing. Originality/value The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.

Mapping Insights from News Articles to Tackle Low Birth Rate and Parenthood in Finland

The aim of this study is to explore how parenthood and birth rate is manifested in Finnish society and citizens as revealed by automated mining of news articles from Finland News API. Several levels of analysis were conducted using natural language processing and text mining techniques to unfold relevant insights from the collected News API. This includes surface-level analysis, word frequency-based analysis, topic-based analysis, and health ontology mapping. In total, 1621 news articles were selected and analyzed from the collected dataset. The surface-level analysis revealed the capacity of imminent health researchers to gain public audience and interest. Content-based analysis revealed the importance of family, employment, and health issues. Topical analysis stressed on the dominance of family issues during corona time, followed by public services and employment issues. Finally, the health ontology mapping revealed the quasi dominance of mental health and development disorders concerns. The research work provides a general framework for analyzing unstructured text to extract useful insights that can help policymakers to positively impact the existing policy in health and social policy development.

Utilising crowdsourcing and text mining to enhance information extraction from social media: A case study in handling COVID-19 supply requests in Thailand

Social media platforms are critical for disaster communication and relief efforts. Rapid and precise social media post analysis is required for effective disaster response. This article presents a comprehensive study of a framework that combines crowdsourcing and text mining techniques to enhance data extraction from social media. The research focuses on a particular case study of COVID-19 pandemic medical supply request, which shows several key findings. First, the incorporation of domain-specific data during the training of named entity recognition (NER) models is essential for accurately identifying and retrieving important entities, such as the names of medical supplies and hospitals. Second, the implementation of a hybrid system leads to improvement in the extraction of information from social media posts. Finally, the involvement of crowdsourcing is found to be significant in the validation, verification, and filtering of disorganised information within the hybrid system. Our performance analysis demonstrates that the use of hybrid models has the potential to significantly improve the extraction of supply names (by up to 37%) and hospital names (by up to 66%), especially in the absence of a comprehensive vocabulary or specially trained NER models. During the COVID-19 supply shortage in Thailand, volunteers utilised hybrid models to expedite the identification of the necessary information. Experiment results demonstrated significant improvement in the accuracy of extracted data, the ability to acquire relevant information in real-time, the capacity to handle a substantial number of posts and the practical benefit of the proposed framework.

Impact of Fintech’s Development on Bank Performance: An Empirical Study from Vietnam

In recent years, fintech has exploded in popularity and importance in the finan- cial industry. Its impacts have spread widely throughout the world, including Vietnam. This study aims to investigate the effect of fintech’s development on bank performance in Vietnam. Based on the unstructured data about fintech on the financial expert web- sites from Vietnam, the word frequency statistic technique of the text mining approach is applied for measuring fintech’s development under the support of Python-based solu- tions. The bank-level data of 15 Vietnamese banks for the period from the first quarter of 2019 to the second quarter of 2021 are collected from the quarterly financial statements in the Vietstock organization. Python programming and text mining techniques are used to compile this dataset by gathering information from popular and relevant websites. The generalized least squares method is used for estimating the panel models. The estimation result shows the significant impact of fintech’s development on bank profitability, but the net interest margin does not associate with the fintech variable. Besides, some interesting findings are revealed: The slow banking transformation to adapt to the rise of fintech and the COVID-19 pandemic increased bank profitability. Furthermore, suggestions for the banks and fintech companies are recommended, and the limitations and directions for further research are also proposed.

Scientific clickbait: Examining media coverage and readability in genome-wide association research.

In the present study, we analyzed a large corpus of English-language online media articles covering genome-wide association studies (GWAS), exemplifying the use of computational methods to study science communication in biological sciences. We analyzed trends in media coverage, readability, themes, and mentions of ethical and social issues, in over 5,000 websites published from 2005 to 2018 from 3,555 GWAS publications on 1,943 different traits, identified via GWAS Catalog using a text-mining approach to inform the discussion about genetic literacy and media coverage. We found that 22.9% of GWAS papers received media attention but most were described in language too complex to be understood by the public. Ethical issues are rarely mentioned and mentions of translation are increasing over time. We predicted media attention based on year of publication, number of genetic associations identified, study sample size, and journal impact factor, using a regression model (r2 = 38.7%). We found that chronotype, educational attainment, alcohol and coffee consumption, sexual orientation, tanning, and hair color received substantially more attention than predicted by the regression model. We also evaluated the prevalence of the clickbait "one gene, one disease" headlines (e.g., "Scientists Say They've Found Gene That Causes Breast Cancer") and found that it is declining. In sum, online media coverage of GWAS should be more accessible, introduce more modern genetics terms, and when appropriate, ELSI should be mentioned. Science communication research can benefit from big data and text-mining techniques which allow us to study trends and changes in coverage trends across thousands of media outlets. Results can be explored interactively in a website we have built for this manuscript: https://jjmorosoli.shinyapps.io/newas/.

Semantic information extraction and search of mineral exploration data using text mining and deep learning methods

Large-scale mineral resource reports offer a wealth of information for geological knowledge mining and mineral explorers’ knowledge discovery. The geological conditions in which mineral deposits develop may be learned a great deal from these mineral exploration reports. Mineral exploration data may be queried and aggregated to effectively mitigate future exploration risks and reduce costs. However, due to the reports being presented in unstructured textual format, it becomes challenging to extract valuable geological data without manually scanning through a vast number of reports. This laborious process poses difficulties for geologists. To address this issue, this paper proposes a system that extracts a set of geologically relevant keywords/keyphrases of each chapter from each mineral exploration reposts using latent Dirichlet allocation (LDA), develops a topic graph, recognizes the geological entity and related relations for constructing a knowledge graph, and uses visualization of those graphs (e.g., topic graphs and knowledge graphs) to explore the contents of the report. The text mining and machine learning technique described here serves as the foundation for future research into incorporating semantic analysis into geological information extraction. The findings of this study show how automated text analysis may help with the quick processing of huge quantities of reports in order to identify target mineral systems and their related geological location and rock mineral composition. The suggested approaches can quickly and reliably convert mineral exploration data (e.g., text, figure, and table) into a structured form, which is a hitherto untouched field in geological knowledge mining.

Presidential Communication and Its Impact on the Mexican Stock Market: Evidence Using a Sentiment Analysis Approach

This research paper addresses the impact of the positive or negative polarity of the presidential messages contained in the stenographic version of the daily conferences issued by the president of Mexico, obtained by means of semantic and machine learning text mining techniques, on the performance of the Mexican Stock Exchange’s Price and Quotations Index (Índice de Precios y Cotizaciones-IPC). Our results provide evidence of the existence of a negative and significant relationship of the polarity of presidential messages with IPC performance, i.e., as the positive polarity of messages increases, IPC performance decreases.

Translation of Metaphorical Information in Japanese Literature Combined with Text Mining Techniques

Abstract Japanese literature is renowned for its conciseness and the profound, multi-layered meanings conveyed through its economical use of language and rich symbolic content. This paper explores the creative impact of these linguistic features in Japanese literature by analyzing the deep meanings and varied classifications of literary metaphors. The construction of a Japanese literature corpus involved the collection, cleaning, and organization of Japanese literary texts, followed by the selection of specific linguistic features. The corpus was then aligned using a dynamic programming approximation algorithm. A semantic representation model of the Japanese literature text corpus was developed by integrating the Attention Word Embedding Model with the Extended Topic Information Model, and metaphoric corpus features were fused using the static fusion strategy of Extended Topic Information and Attention Word Embedding. This study also examines the translation of symbolic information in Japanese literature, focusing on linguistic and stylistic features. The findings reveal that translations into Chinese tend to employ augmentation translation, resulting in 1,706 additional sentences compared to the original Japanese texts. Furthermore, the lexical density in the Chinese translation reference corpus is 2.45 percentage points higher than in the Japanese literature corpus. Analysis of verb modifications shows that among the 90 altered verbs in the Chinese translation corpus, only 24 have corresponding instances in the original Japanese texts. Text mining techniques were employed to conduct a deep analysis of symbolic content, enhancing the understanding of the nuanced connotations inherent in Japanese literature.

A Research-Based Ontology for Collaborative Innovation: A Methodology Leveraging AI and Domain Expert Knowledge

This paper introduces a method, for creating a research driven ontology to foster collaboration and innovation. The suggested approach combines intelligence (AI) and Expert knowledge to build a comprehensive model encompassing various aspects of research, development and innovation. To demonstrate the feasibility of this method the paper showcases its implementation, in the field of accounting science. First, AI-powered machine learning algorithms and text mining techniques are used to extract the main ontological elements from a large corpus of accounting literature. Subsequently, expert knowledge is utilized to refine and validate these identified elements. The resulting ontology can be used as the foundation of a knowledge-based system to promote collaboration and analyze the state of innovation.

Text Mining - A Comparative Review of Twitter Sentiments Analysis

Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with AdaBoost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.

Human reliability of the intelligent construction site tower crane driver interface based on DEMATEL-ISM-BN.

With the arrival of Industry 4.0, intelligent construction sites have seen significant development in China. However, accidents involving digitized tower cranes equipped with smart systems continue to occur frequently. Among the main causes of these accidents is human unsafe behavior. To assess the human factors reliability of intelligent construction site tower cranes, it is necessary to shift the safety focus to the human-machine interface and identify patterns of human error behaviors among tower crane drivers through text mining techniques (TF-IDF-TruncatedSVD-ComplementNB). Based on the SHEL model, the behavioral factors influencing human factors reliability in the human-machine interface are categorized and a Performance Shaping Factors (PSF) system is constructed. Building on the foundation of constructing an indicator system for human factors error influence in the driver interface of intelligent construction site tower cranes, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is combined with the Interpretive Structural Modeling (ISM) to analyze the importance of various factors in causing human errors and to analyze the logical structure among these factors. Simultaneously, a Bayesian network is constructed using a multi-level hierarchical structural model, thus establishing a new evaluation method for the human-machine interface. The effectiveness of the proposed method is validated through Bayesian network causal inference based on real case studies. The results demonstrate that the evaluation process of this method aligns with the operational scenarios of tower crane drivers in intelligent construction sites. It not only allows for quantifying the likelihood of human errors but also enables the development of targeted measures for controlling unsafe behaviors among tower crane drivers in intelligent construction sites.