Using Word Association to Detect Multitopic Structures in Text Documents

Klahold, André; Uhr, Patrick; Ansari, Fazel; Fathi, Madjid

doi:10.1109/mis.2013.120

Cited by 21 publications

(17 citation statements)

References 16 publications

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“…a wordlist), with evaluated entries ( ) and finishes at . For the damping factor values in the interval [0.4 -0.6] achieved best results in previous studies [16]. For this application damping factor is used.…”

Section: B Applying the Associaton Concept Cimawa For Sentimentmentioning

confidence: 99%

Sentiment analysis in financial markets A framework to utilize the human ability of word association for analyzing stock market news reports

Uhr

Zenkert

Fathi

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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As financial markets getting faster and more complex, it is difficult for market participants to manage the information overload. Sentiment analysis is a useful text mining method to process textual content and filter the results with analysis methods to relevant and meaningful information. The paper in hand introduces a new method for sentiment analysis in financial markets which combines word associations and lexical resources. Based on stock market news from January 2000 to February 2014 we analyzed documents on different levels. The results are presented and evaluated in this paper.

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Section: B Applying the Associaton Concept Cimawa For Sentimentmentioning

confidence: 99%

Sentiment analysis in financial markets A framework to utilize the human ability of word association for analyzing stock market news reports

Uhr

Zenkert

Fathi

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…The unstructured or semi-structured maintenance records are, for example, text reports or emails captured via reporting and documentation tools, or audio or images collected by means of microphones and cameras, respectively. The pre-processing time may vary depending on the volume and quality of unstructured temporal data using text-mining approaches introduced in (Klahold et al 2013) and (Ansari, Uhr, and Fathi 2014), or signal and/or image processing algorithms discussed in (Perner 2008). Notably, the present version of PriMa deals only with one type of unstructured data (i.e.…”

Section: Description Of the Modelmentioning

confidence: 99%

“…Moreover, extracting and learning new concepts and knowledge from textual data is supported by textual-meta analytic algorithms. In particular, word associative measuring and associative gravity force calculation are employed, which has been introduced and evaluated in (Klahold et al 2013) and (Ansari, Uhr, and Fathi 2014) (cf. Section 4.3.2).…”

Section: Description Of the Modelmentioning

confidence: 99%

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PriMa: a prescriptive maintenance model for cyber-physical production systems

Ansari

Grepl

Nemeth

2019

International Journal of Computer Integrated Manufacturing

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134

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Cyber-physical production systems (CPPS), as an emerging Industry 4.0's technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined. ARTICLE HISTORY

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“…For the detection of multiple topics within text document structures, Klahold et al defined associative gravity as a new method to separate documents into topic-related clusters [44]. The method is based on the text mining method entitled CIMAWA, which imitates the human ability of word association [45].…”

Section: Topic Detectionmentioning

confidence: 99%

Knowledge discovery in multidimensional knowledge representation framework

2018

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Visualization of results is one of the central challenges in big data analytics and integrative text mining. With a growing amount of unstructured data and different perspectives on big data, knowledge graphs have difficulties to simultaneously represent and visualize all analyzed dimensions of knowledge. This paper proposes integrative text mining as a solution to combine results from different dimensional analysis in a multidimensional knowledge representation (MKR) for knowledge discovery and visualization purpose. Analysis results from named entity recognition, topic detection, sentiment analysis and the extraction of semantic relationships are, therefore, integrated into a common representation structure. In the implementation part of this research, an application is introduced which utilizes MKR based on the results of stated text mining methods applied on a German and English news data set. State-of-the-art visualizations are used in the application and MKR adaptively transforms the visualization type of the knowledge graph according to the selected context for knowledge discovery.

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Using Word Association to Detect Multitopic Structures in Text Documents

Cited by 21 publications

References 16 publications

Sentiment analysis in financial markets A framework to utilize the human ability of word association for analyzing stock market news reports

Sentiment analysis in financial markets A framework to utilize the human ability of word association for analyzing stock market news reports

PriMa: a prescriptive maintenance model for cyber-physical production systems

Knowledge discovery in multidimensional knowledge representation framework

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