TEXT MINING FOR TECHNOLOGY ROADMAPPING — THE STRATEGIC VALUE OF INFORMATION

2014 ◽  
Vol 18 (03) ◽  
pp. 1440004 ◽  
Author(s):  
VICTORIA KAYSER ◽  
KERSTIN GOLUCHOWICZ ◽  
ANTJE BIERWISCH
Keyword(s):  
Text Mining ◽  
Process Model ◽  
Strategy Development ◽  
External Information ◽  
Environmental Scanning ◽  
Technology Roadmapping ◽  
Strategic Value ◽  
Alternative Future ◽  
Textual Data ◽  
Structured Analysis

Technology roadmapping is a well-established method used in strategy development to map alternative future paths, while text mining offers untapped potentials concerning early detection and environmental scanning. In this paper, the roadmapping process is split into different steps in order to analyse which text mining methods could add further value within each. This leads to a two-layered process model, which includes text mining techniques to systematically integrate external information in ongoing roadmapping processes. Textual data can be used for a structured analysis and exploration of thematic fields and an objective, quantitative summary of actual developments. To demonstrate some of the benefits, the field of "cloud computing" is used to illustrate the procedure. As this article will show, the results provided by this approach extend the existing methodology, integrates an external view and complements expert opinion.

Contextualized Meaning Extraction

2017 ◽  
Vol 7 (3) ◽  
pp. 15-29
Author(s):  
Jeffrey D. Wall ◽  
Rahul Singh
Keyword(s):  
Decision Making ◽  
Text Mining ◽  
Business Intelligence ◽  
Process Model ◽  
Semantic Analysis ◽  
Accurate Information ◽  
Semantic Features ◽  
Textual Data ◽  
Mining Algorithms ◽  
Insight Into

Text mining is a powerful form of business intelligence that is used increasingly to inform organizational decisions. Current text mining algorithms rely heavily on the lexical, syntactic, structural, and semantic features of text to extract meaning and insight for decision making. Although semantic analysis is a useful approach to meaning extraction, pragmatics suggests that a more accurate meaning of text can be extracted by examining the context in which the text is recorded. Given that massive amounts of textual data can be drawn from multiple and diverse sources, accounting for context is increasingly important. A conceptual model is provided to explain how concepts from pragmatics can improve existing text mining algorithms to provide more accurate information for decision making. Reversing the pragmatic process of meaning expression could lead to improved text mining algorithms. The theoretical process model developed herein can provide insight into the development and refinement of text mining algorithms that draw from diverse sources.

scholarly journals Combining Text Mining and Data Visualization Techniques to Understand Consumer Experiences of Electronic Cigarettes and Hookah in Online Forums

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Annie T. Chen ◽  
Shu-Hong Zhu ◽  
Mike Conway
Keyword(s):  
Text Mining ◽  
Data Visualization ◽  
Electronic Cigarettes ◽  
Discussion Forums ◽  
Online Forums ◽  
Textual Data ◽  
Consumer Experiences ◽  
Visualization Techniques

Our aim in this work is to apply text mining and novel visualization techniques to textual data derived from online health discussion forums in order to better understand consumers experiences and perceptions of electronic cigarettes and hookah.

scholarly journals A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria

2020 ◽  
pp. 73-107
Author(s):  
Mohammed M. Tumala ◽  
Babatunde S. Omotosho
Keyword(s):  
Monetary Policy ◽  
Text Mining ◽  
Central Bank ◽  
Communication Strategy ◽  
Central Bank Communication ◽  
Monetary Policy Committee ◽  
Textual Data ◽  
Policy Communication ◽  
Policy Objectives ◽  
Monetary Policy Transparency

This paper employs text-mining techniques to analyse the communication strategy of the Central Bank of Nigeria (CBN) during the period 2004-2019. Since the policy communique released after each meeting of the CBN’s monetary policy committee (MPC) represents an important tool of central bank communication, we construct a corpus based on 87 policy communiques with a total of 123, 353 words. Having processed the textual data into a form suitable for analysis, we examined the readability, sentiments, and topics of the policy documents. While the CBN’s communication has increased substantially over the years, implying increased monetary policy transparency; the computed Coleman and Liau readability index shows that the word and sentence structures of the policy communiques have become more complex, thus reducing its readability. In terms of monetary policy sentiments, we find an average net score of -10.5 per cent, reflecting the level of policy uncertainties faced by the MPC over the sample period. In addition, our results indicate that the topics driving the linguistic contents of the communiques were influenced by the Bank’s policy objectives as well as the nature of shocks hitting the economy per period.

An SAO-based text-mining approach for technology roadmapping using patent information

2012 ◽  
Vol 43 (1) ◽  
pp. 52-74 ◽  
Author(s):  
Sungchul Choi ◽  
Hongbin Kim ◽  
Janghyeok Yoon ◽  
Kwangsoo Kim ◽  
Jae Yeol Lee
Keyword(s):  
Text Mining ◽  
Technology Roadmapping ◽  
Patent Information

Discovering a Hierarchical Design Process Model Using Text Mining

2016 ◽  
Author(s):  
Lijun Lan ◽  
Ying Liu ◽  
Wen Feng Lu
Keyword(s):  
Text Mining ◽  
Design Process ◽  
Process Model ◽  
Entity Recognition ◽  
Coarse Grained ◽  
Design Information ◽  
Design Project ◽  
Design Process Model ◽  
Process Oriented ◽  
Design Documents

The increasing design documents created in the design process provide a useful source of process-oriented design information. Hence, the need for automated design information extraction using advanced text mining techniques is increasing. However, most of the existing text mining approaches have problems in mining design information in depth, which results in low efficiency in applying the discovered information to improve the design project. With the aim of extracting process-oriented design information from design documents in depth, this paper proposes a layered text mining approach that produces a hierarchical process model which captures the process behavior at the different level of details. Our approach consists of several interrelated algorithms, namely, a content-based document clustering algorithm, a hybrid named entity recognition (NER) algorithm and a frequency-based entity relationship detection method, which have been integrated into a system architecture for extracting design information from coarse-grained views to fine-grained specifications. To evaluate the performance of the proposed algorithms, experiments were conducted on an email archive that was collected from a real-life design project. The results showed an increase in the detection accuracy for the process-oriented information detection.

Ooredoo Rayek

2020 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Badia Klouche ◽  
Sidi Mohamed Benslimane ◽  
Sakina Rim Bennabi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Support Vector ◽  
Textual Data ◽  
New Strategy ◽  
Set Up

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

The Method to Analyze Freely Described Data from Questionnaires

2009 ◽  
Vol 13 (3) ◽  
pp. 268-274 ◽  
Author(s):  
Masaomi Kimura ◽  
Keyword(s):  
Text Mining ◽  
Call Center ◽  
Clustering Algorithms ◽  
Web Pages ◽  
Research Papers ◽  
Text Data ◽  
Textual Data ◽  
The Way ◽  
Newspaper Articles

Text mining has been growing; mainly due to the need to extract useful information from vast amounts of textual data. Our target here is text data, a collection of freely described data from questionnaires. Unlike research papers, newspaper articles, call-center logs and web pages, which are usually the targets of text mining analysis, the freely described data contained in the questionnaire responses have specific characteristics, including a small number of short sentences forming individual pieces of data, while the wide variety of content precludes the applications of clustering algorithms used to classify the same. In this paper, we suggest the way to extract the opinions which are delivered by multiple respondents, based on the modification relationships included in each sentence in the freely described data. Certain applications of our method are also presented after the introduction of our approach.

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