A Survey of Topic Modeling in Text Mining

Alghamdi, Rubayyi; Alfalqi, Khalid

doi:10.14569/ijacsa.2015.060121

Cited by 241 publications

(180 citation statements)

References 22 publications

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“…Based on this assumption, topic models have been devised as probabilistic models that attempt to find a set of words from the collection of documents that best explain the topics in documents. By discovering hidden relationships between words and topics across the collection of documents, topic models help us to identify documents that are similar in the use of words (i.e., topics) and annotate documents according to these topics [82]. Hence, the topic-based approach has provided a convenient way to organize, understand, and summarize a large collection of textual information in various fields [83].…”

Section: Lda For Topic Analysismentioning

confidence: 99%

Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis

Choi

Song

2018

Sustainability

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Abstract:With the strategic importance of discerning opportunities and threats from technological development to achieve sustainable competitiveness, exploring technological trends becomes critical for a successful technology strategy in logistics. Given the rapid pace of development and varying technological options, logistics also increasingly requires methodological support and appropriate data to reduce the complexity and burden of exploring technology trends. While previous research has largely relied on experts' insights, the value of patent-based approaches for exploring technological trends has been underestimated in logistics. To address this gap, this study proposes a topic modeling-based approach using logistics-related patents registered at the United States Patents and Trademark Office (USPTO). The core of the suggested approach is latent Dirichlet allocation (LDA), allowing the identification of logistics-related technological topics behind patents. The topics identified by LDA are further investigated regarding both filed-level and firm-level trends. The suggested approach is expected to offer implications of the use of patents for the purpose of exploring the trends of technology development outside the organization in logistics. In addition, we believe that the information on the technological topics and their trends generated by the suggested approach can offer an enhanced understanding of the technological landscape in logistics.

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Section: Lda For Topic Analysismentioning

confidence: 99%

Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis

Choi

Song

2018

Sustainability

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“…Within a particular domain, researchers are increasingly interested in exploring scientific literature to gain insights on how research develops and evolves over time [24]. For instance, this kind of analytical data-driven insight can benefit researchers as they delve into new areas by providing knowledge of current popular topics and how the focus on different topics has shifted through time [1,24]. While the advent of digital publishing and open access science have led to greater access to scientific content, the sheer volume has made it very difficult for researchers to analyze literature at a high level and identify temporal trends in the evolution of research areas.…”

Section: Introductionmentioning

confidence: 99%

“…Each document in a corpus is represented as a probabilistic mixture of topics while each topic consists of a mixture of words. In this manner, topic modeling algorithms discover patterns in textual data via topic generation and use those topics to connect documents with similar content [1]. This approach of analyzing text has been used in disparate domains such as social sciences, business analytics, and computer science.…”

Section: Introductionmentioning

confidence: 99%

What’s Hot and What’s Not? - Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis

Hahn¹,

Mohanty²,

Manda³

2017

Lecture Notes in Computer Science

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Abstract. Scientists exploring a new area of research are interested to know the "hot" topics in that area in order to make informed choices. With exponential growth in scientific literature, identifying such trends manually is not easy. Topic modeling has emerged as an effective approach to analyze large volumes of text. While this approach has been applied on literature in other scientific areas, there has been no formal analysis of bioinformatics literature.Here, we conduct keyword and topic model-based analysis on bioinformatics literature starting from 1998 to 2016. We identify top keywords and topics per year and explore temporal popularity trends of those keywords/areas. Network analysis was conducted to identify clusters of sub-areas/topics in bioinformatics. We found that "big-data", "next generation sequencing", and "cancer" all experienced exponential increase in popularity over the years. On the other hand, interest in drug discovery has plateaued after the early 2000s.

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“…Figure 1 illustrates types of entities and research products in the EDA landscape, along with the "life cycle of ideas" seen at major conferences such as the Design Automation Conference. 1 Uncovering "research impact" requires understanding (identification, characterization) of the edges and feedback paths that we have omitted from the figure (e.g., edges from conference papers to journal papers, or from patents to publications; paths from consortia research needs through to papers and EDA startups; etc.). conferences is also shown.…”

Section: Introductionmentioning

confidence: 99%

“…(joined in recent years by "test of time" awards). At the same time, any ability to measure the impact of DA research has obvious potential benefit 1 for the field, e.g., (i) as the basis for early identification of highimpact or high-value research directions and results; (ii) as part of a feedback or learning loop that helps funding agencies create higher-impact research programs with limited resources; or (iii) as motivation for increased overall investment in DA research if the ROI is sufficiently high.…”

Section: Introductionmentioning

confidence: 99%

Toward metrics of design automation research impact

Kahng¹,

Luo²,

Nam³

et al. 2015

2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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Design automation (DA) research has for over fifty years been performed in academia, semiconductor and system companies, and EDA companies worldwide. This research has been enabling to continued scaling of design productivity and growth of the semiconductor industry. For product companies, funding program managers and individual researchers alike, a highly relevant question is: what DA research, and what DA research outcomes, ultimately have the greatest "impact"? In this paper, we present measurements and analyses of DA research outputs (papers, patents, EDA companies), upon which future metrics of DA research impact might be based. Our studies consider 47000+ conference and journal papers from 1964-2014; the inter-patent citation graph over 759000+ DA-related patents; abstracts of 1150+ U.S. NSF projects over a three-decade span; 36 research needs documents of the Semiconductor Research Corporation from 2000-2013; and market segmentation of hundreds of EDA companies. We identify several interesting correlations, but do not claim to identify causal relationships; indeed, connecting traditional measures of research output to real-world impacts seems quite challenging. We conclude with several directions and targets for future investigation.

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A Survey of Topic Modeling in Text Mining

Abstract: Abstract-Topic

Cited by 241 publications

References 22 publications

Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis

Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis

What’s Hot and What’s Not? - Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis

Toward metrics of design automation research impact

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