In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining

Text mining utilizes machine learning (ML) and natural language processing (NLP) for text implicit knowledge recognition, such knowledge serves many domains as translation, media searching, and business decision making. Opinion mining (OM) is one of the promised text mining fields, which are used for polarity discovering via text and has terminus benefits for business. ML techniques are divided into two approaches: supervised and unsupervised learning, since we herein testified an OM feature selection(FS)using four ML techniques. In this paper, we had implemented number of experiments via four machine learning techniques on the same three Arabic language corpora. This paper aims at increasing the accuracy of opinion highlighting on Arabic language, by using enhanced feature selection approaches. FS proposed model is adopted for enhancing opinion highlighting purpose. The experimental results show the outperformance of the proposed approaches in variant levels of supervisory,i.e. different techniques via distinct data domains. Multiple levels of comparison are carried out and discussed for further understanding of the impact of proposed model on several ML techniques.

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Modern Clinical Text Mining: A Guide and Review

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-030421-030931 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Bethany Percha

Keyword(s):

Machine Learning ◽

Text Mining ◽

Data Science ◽

Annual Review ◽

Publication Date ◽

Biomedical Data ◽

Clinical Text ◽

Quality Improvement Research ◽

Comprehensive Survey ◽

Technical Advances

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Opening the Knowledge Tombs - Web Based Text Mining as Approach for Re-evaluation of Machine Learning Rules

Advances in Databases and Information Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-642-15576-5_40 ◽

2010 ◽

pp. 533-542 ◽

Cited By ~ 1

Author(s):

Milan Zorman ◽

Sandi Pohorec ◽

Boštjan Brumen

Keyword(s):

Machine Learning ◽

Text Mining ◽

Web Based ◽

Learning Rules

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Detection of Economy-Related Turkish Tweets Based on Machine Learning Approaches

10.4018/978-1-7998-8413-2.ch008 ◽

2022 ◽

pp. 171-195

Author(s):

Jale Bektaş

Keyword(s):

Machine Learning ◽

Text Mining ◽

Text Classification ◽

Integration Method ◽

Classification Problem ◽

Feature Representation ◽

Learning Approaches ◽

Machine Learning Methods ◽

Linguistic Approach ◽

Turkish Language

Conducting NLP for Turkish is a lot harder than other Latin-based languages such as English. In this study, by using text mining techniques, a pre-processing frame is conducted in which TF-IDF values are calculated in accordance with a linguistic approach on 7,731 tweets shared by 13 famous economists in Turkey, retrieved from Twitter. Then, the classification results are compared with four common machine learning methods (SVM, Naive Bayes, LR, and integration LR with SVM). The features represented by the TF-IDF are experimented in different N-grams. The findings show the success of a text classification problem is relative with the feature representation methods, and the performance superiority of SVM is better compared to other ML methods with unigram feature representation. The best results are obtained via the integration method of SVM with LR with the Acc of 82.9%. These results show that these methodologies are satisfying for the Turkish language.

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ADVANCING AN INTERDISCIPLINARY SCIENCE OF AGING THROUGH A PRACTICE-BASED DATA SCIENCE APPROACH

Innovation in Aging ◽

10.1093/geroni/igz038.1786 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S480-S480

Author(s):

Robert Lucero ◽

Ragnhildur Bjarnadottir

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Older Adults ◽

Text Mining ◽

Language Processing ◽

Fall Risk ◽

Data Science ◽

Care Quality ◽

Science Approach ◽

Hospitalized Older Adults

Abstract Two hundred and fifty thousand older adults die annually in United States hospitals because of iatrogenic conditions (ICs). Clinicians, aging experts, patient advocates and federal policy makers agree that there is a need to enhance the safety of hospitalized older adults through improved identification and prevention of ICs. To this end, we are building a research program with the goal of enhancing the safety of hospitalized older adults by reducing ICs through an effective learning health system. Leveraging unique electronic data and healthcare system and human resources at the University of Florida, we are applying a state-of-the-art practice-based data science approach to identify risk factors of ICs (e.g., falls) from structured (i.e., nursing, clinical, administrative) and unstructured or text (i.e., registered nurse’s progress notes) data. Our interdisciplinary academic-clinical partnership includes scientific and clinical experts in patient safety, care quality, health outcomes, nursing and health informatics, natural language processing, data science, aging, standardized terminology, clinical decision support, statistics, machine learning, and hospital operations. Results to date have uncovered previously unknown fall risk factors within nursing (i.e., physical therapy initiation), clinical (i.e., number of fall risk increasing drugs, hemoglobin level), and administrative (i.e., Charlson Comorbidity Index, nurse skill mix, and registered nurse staffing ratio) structured data as well as patient cognitive, environmental, workflow, and communication factors in text data. The application of data science methods (i.e., machine learning and text-mining) and findings from this research will be used to develop text-mining pipelines to support sustained data-driven interdisciplinary aging studies to reduce ICs.

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