A research case study: Difficulties and recommendations when using a textual data mining tool

Al-Hassan, Abeer A.; Alshameri, Faleh; Sibley, Edgar H.

doi:10.1016/j.im.2013.05.010

Cited by 6 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AleEbrahim and Fathian ( 2013 ) develop a method to summarize customer online reviews from websites. Al-Hassan et al ( 2013 ) investigate whether the North American Industry Classification System code (NAICS) effectively shows the true industrial sectors of Fortune 500 firms by analyzing their websites. Battistini et al ( 2013 ) present a technique to map geo-tagged geo-hazards, such as landslides, earthquakes and floods, by analyzing online news.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of web mining in studying innovation

2014

View full text Add to dashboard Cite

As enterprises expand and post increasing information about their business activities on their websites, website data promises to be a valuable source for investigating innovation. This article examines the practicalities and effectiveness of web mining as a research method for innovation studies. We use web mining to explore the R&D activities of 296 UK-based green goods small and mid-size enterprises. We find that website data offers additional insights when compared with other traditional unobtrusive research methods, such as patent and publication analysis. We examine the strengths and limitations of enterprise innovation web mining in terms of a wide range of data quality dimensions, including accuracy, completeness, currency, quantity, flexibility and accessibility. We observe that far more companies in our sample report undertaking R&D activities on their web sites than would be suggested by looking only at conventional data sources. While traditional methods offer information about the early phases of R&D and invention through publications and patents, web mining offers insights that are more downstream in the innovation process. Handling website data is not as easy as alternative data sources, and care needs to be taken in executing search strategies. Website information is also self-reported and companies may vary in their motivations for posting (or not posting) information about their activities on websites. Nonetheless, we find that web mining is a significant and useful complement to current methods, as well as offering novel insights not easily obtained from other unobtrusive sources.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Use of web mining in studying innovation

2014

View full text Add to dashboard Cite

show abstract

“…Large and semi-formatted datasets can be efficiently and effectively extracted through a variety of computational techniques. Clustering algorithms provide a relational picture, improving analysis by showing characteristics that provide meaning and determine the most important features (Al-Hassan, Alshameri & Sibley, 2013).…”

Section: Textual Data Miningmentioning

confidence: 99%

Analyzing The Strength Between Mission And Vision Statements And Industry Via Machine Learning

Alshameri

Green

2020

JABR

Self Cite

View full text Add to dashboard Cite

Mission and vision statements are critical to a company’s success both from a company’s long-term goals and appearance to potential customers. We analyze a collection of 772 mission and vision statements from companies via natural language processing. This data is hand annotated into 15 industry types. We show the distinctiveness and connectiveness of each industry via text processing and machine learning techniques. The extracted features of each industry are a telling and guiding indicator of what that industry embraces. We show high predictive power via machine learning to determine an industry by looking only at the mission and vision statements

show abstract

“…Text mining is a knowledge discovery technology that enables researchers to discern patterns and trends based on unstructured text. It is possible to extract hidden knowledge using approaches such as natural language analysis, information retrieval, information extraction, and data mining [17,[39][40][41][42][43][44][45][46]. Patent documents contain lengthy and rich explanations in technical and legal terminologies [47,48].…”

Section: Topic Modeling Of Patentsmentioning

confidence: 99%

Identifying Emerging Trends of Financial Business Method Patents

Lee

Sohn

2017

Sustainability

View full text Add to dashboard Cite

Financial technology has become an important part of the banking industry in recent times. This study attempts to propose a framework to identify emerging areas and trends using financial business method patents. Based on the abstracts of financial business method patents registered at the United States Patent and Trademark Office, this study first applies latent Dirichlet allocation to identify emerging topics. The probability of the annual occurrence of each topic is adjusted through the exponentially weighted moving average to reflect the importance of the recent probability of topics. Each topic is classified as "hot" or "cold" depending on whether the exponentially weighted moving average of the probabilities exceeds the threshold. We applied survival analysis to the time gap of recurrently becoming hot from a cold status with the associated factor of financial business method patents. The findings suggest that the topic with the short granted period and high forward citation is likely to become hot. In addition, the topic that is aged and specific in narrow areas is likely to continuously change into the hot or cold status. The approach proposed in this study contributes toward understanding topic emergence in the financial area and pursuing sustainable development.

show abstract

A research case study: Difficulties and recommendations when using a textual data mining tool

Cited by 6 publications

References 3 publications

Use of web mining in studying innovation

Use of web mining in studying innovation

Analyzing The Strength Between Mission And Vision Statements And Industry Via Machine Learning

Identifying Emerging Trends of Financial Business Method Patents

Contact Info

Product

Resources

About