Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads

2021 ◽  
Author(s):  
David M. Goldberg ◽  
Nohel Zaman ◽  
Arin Brahma ◽  
Mariano Aloiso
Keyword(s):  
Text Mining ◽  
Mortgage Loan ◽  
Predictive Analysis ◽  
Discussion Threads

Discovering genres of online discussion threads via text mining

2009 ◽  
Vol 52 (2) ◽  
pp. 481-495 ◽  
Author(s):  
Fu-Ren Lin ◽  
Lu-Shih Hsieh ◽  
Fu-Tai Chuang
Keyword(s):  
Text Mining ◽  
Online Discussion ◽  
Discussion Threads

scholarly journals Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM

2021 ◽  
Vol 3 (1) ◽  
pp. 9-20
Author(s):  
Titin Handayani Sinaga ◽  
Anjar Wanto ◽  
Indra Gunawan ◽  
Sumarno Sumarno ◽  
Zulaini Masruro Nasution
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Customer Satisfaction ◽  
Computer Science ◽  
Corporate Image ◽  
Predictive Analysis ◽  
Data Sets ◽  
C4.5 Algorithm ◽  
Intelligent Methods

This application applies the C4.5 Algorithm to decide customer satisfaction, the C4.5 algorithm is one of the algorithms used to classify or segment, or group and it is predictive. This type of research is a classification with the concept of data mining involving 150 customers of PDAM Tirta Lihou in Totap Majawa Kab. Simalungun can be categorized as: "Satisfied and Dissatisfied". The meaning of Data Mining is an interdisciplinary subfield of computer science and statistics with the overall objective of extracting information (with intelligent methods) from data sets and converting information into understandable structures for further use. There are 5 criteria that can affect customer satisfaction, among others: Service Facilities (x1), Price Rates (x2), Smooth Water (x3), Corporate Image (x4), and Location (x5). The results of processing the C4.5 method using the RapidMiner Studio 5.3 software mean that Rapid Miner is a solution for analyzing data mining, text mining, and predictive analysis. Rapid Miner uses various descriptive and predictive techniques in providing insight to users so that they can make the best decisions with the level of accuracy, namely, class recall and class precision values, it is explained that the "Satisfied" category produces a class recall of 97.80% and a class precision of 97.80%. 98.89% and the "Not Satisfied" category resulted in a class recall of 98.31% and a class of precision of 96.67%. And the above accuracy results from the calculation of the C4.5 algorithm is 98.0%. Keywords: C4.5 Algorithm, Data Mining, Customer Satisfaction, PDAM Tirta Lihou

Using Text Mining to Characterize Online Discussion Facilitation

2011 ◽  
Vol 15 (2) ◽  
Author(s):  
Norma Ming ◽  
Eric P.S. Baumer
Keyword(s):  
Text Mining ◽  
Semantic Analysis ◽  
Analytical Techniques ◽  
Online Discussions ◽  
Faculty Interaction ◽  
Discussion Threads ◽  
Potential Applications ◽  
Undergraduate Science ◽  
Science Classes ◽  
Productive Discussion

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help discover meaningful patterns in the discussions more efficiently. Based on a close reading of selected discussion threads from online undergraduate science classes, we observed a variety of facilitation strategies associated with discussion quality. These observations informed our selection of a larger dataset of discussion threads to analyze via text mining techniques. Using latent semantic analysis to produce topic models of the content of the discussions, we constructed visualizations of the topical and temporal development of those discussions among students and faculty. These visualizations revealed patterns that appeared to correspond with specific facilitation styles and with the extent to which discussions remained focused on particular topics. From a case study focusing on six of these discussions, we documented distinct patterns in the types of facilitation strategies employed and the character of the discussions that followed. In our conclusion, we discuss potential applications of these analytical techniques for helping students, faculty, and faculty developers become more aware of their participation and influence in online discussions, thereby improving their value as a learning environment.

Science and technology text mining:: Text Mining of the Journal Cortex

2013 ◽  
Author(s):  
Ronald N. Kostoff ◽  
◽  
Henry A. Buchtel ◽  
John Andrews ◽  
Kirstin M. Pfiel
Keyword(s):  
Text Mining ◽  
Science And Technology

MONITORING OF OPERATIONAL LOGISTIC PROCESSES IN GENERAL CARGO WAREHOUSES USING PREDICTIVE ANALYSIS

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert
Keyword(s):  
Data Mining ◽  
Human Error ◽  
Predictive Analysis ◽  
Data Mining Algorithm ◽  
It Infrastructure ◽  
Monitoring Procedure ◽  
Manual Recording ◽  
Mining Tool ◽  
General Cargo ◽  
Different Characteristics

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

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