Model degradation in web derived text-based models

Daas, Piet; Jansen, Jelmer

doi:10.4995/carma2020.2020.11560

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…However, a major challenge was the model stability, as the accuracy of the model dropped from 93% to 63% after one year. Therefore, Daas and Jansen (2020) dove deeper into this model stability issue. They explained how disappearing websites on one hand, and updated websites on the other hand contribute to this model degradation.…”

Section: Related Researchmentioning

confidence: 99%

Topic modelling applied on innovation studies of Flemish companies

Crijns

Vanhullebusch

Reusens

et al. 2023

Journal of Business Analytics

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Mapping innovation in companies for the purpose of official statistics is usually done through business surveys. However, this traditional approach faces several drawbacks like a lack of responses, response bias, low frequency, and high costs. Therefore, possible solutions like text-based models have been developed to complement or substitute traditional business surveys. Web scraped company websites are used as input texts for these models. Previous research often focuses on the classification of companies into innovative or non-innovative through these models. This paper makes use of web scraping and text-based models in order to map the business innovation in Flanders. What differentiates this research from previously published work, is the special focus on the different types of innovation, discovered through topic modeling. More specifically, the scraped web texts are used to identify innovative economic sectors or topics, and to classify firms into these topics. The Flemish firms considered in this research are those that participated in the CIS 2019. It was found that the Top2Vec model can discover topics related to innovation within a large unstructured text corpus. Consequently, the Lbl2Vec model can use the Top2Vec output as an input to classify firms into the discovered topics. Therefore, this paper shows the potential of combining Top2Vec and Lbl2Vec model for discovering topics (or sectors) and classifying companies into these topics which results in an additional parameter for mapping innovation in different regions.

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Section: Related Researchmentioning

confidence: 99%

Topic modelling applied on innovation studies of Flemish companies

Crijns

Vanhullebusch

Reusens

et al. 2023

Journal of Business Analytics

View full text Add to dashboard Cite

show abstract

Detecting innovative companies via their website

Daas

Doef

2020

SJI

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Producing an overview of innovative companies in a country is a challenging task. Traditionally, this is done by sending a questionnaire to a sample of companies. This approach, however, usually only focuses on large companies. We therefore investigated an alternative approach: determining if a company is innovative by studying the text on its website. For this task a model was developed based on the texts of the websites of companies included in the Community Innovation Survey of the Netherlands. The latter is a survey carried out every two years that focusses on the detection of innovative companies with 10 or more working persons. We found that the text-based model developed was able to reproduce the result from the Community Innovation Survey and was also able to detect innovative companies with less than 10 employees, such as startups. Model stability, model bias, the minimal number of words extracted from a website and companies without a website were found to be important issues in producing high quality results. How these issues were dealt with and the findings on the number of innovative companies with large and small numbers of employees are discussed in the paper.

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Model degradation in web derived text-based models

Cited by 2 publications

References 12 publications

Topic modelling applied on innovation studies of Flemish companies

Topic modelling applied on innovation studies of Flemish companies

Detecting innovative companies via their website

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