Evaluating the competitiveness of enterprise’s technology based on LDA topic model

A topic modelling analysis of white papers in security token offerings: Which topic matters for funding?

“…This process reduces researcher bias because foreknowledge of document content does not affect the topic classifications (Zhang et al, 2021). The LDA topic model is widely used in patent content analysis (Wang et al, 2015;Zhang et al, 2021) and technology topics evaluation (Li et al, 2021;Wang et al, 2020;Savin et al, 2022aSavin et al, , 2022b In order to apply LDA to the STO white papers, we first pre-processed the corpus by 1) converting words to lowercase, 2) removing standard English stop words and punctuation, and 3) lemmatizing all the words by means of the Natural Language Toolkit 6 lemmatiser. We then analysed the distribution of terms with domain experts and filtered out generic terms that appeared in more than 60% of the white papers (Zhang et al, 2021).…”

A Big Data-Driven Approach to Analyze the Influencing Factors of Enterprise’s Technological Innovation

“…e semantic distance between two concepts was obtained by traversing the sum of the weights of the connection paths instead of calculating the number of edges connecting the two concepts. e specific calculation is shown in equation (1). en, calculate the semantic relatedness of concepts through the co-occurrence in the text.…”

“…Text mining, as a representative of the intelligent measure method, has been widely applied in various fields. For example, the evaluation of business intelligence and enterprise technology analysis [1][2][3], the enterprise technology opportunity identification [4,5], correlation analysis of enterprise technology cooperation behavior [6][7][8], the analysis of enterprises technology maturity [9][10][11], and prediction of enterprise technology development trend, etc. [12][13][14].…”

Value creation in emerging technologies through text mining: the case of blockchain

“…As an example, technical keywords may be extracted automatically from the fulltext of patents, their frequency measured using Term Frequency-Inverted Document Frequency metrics, their meaning disambiguated using general lexicons such as WordNet (Joung and Kim 2017). Words can be extracted and clustered using Topic Modelling techniques (Kyebambe et al 2017;Kay, Kim, and Kang 2019;Son, Kim, and Kim 2020;Wang et al 2020;Ebadi et al 2020) while network maps can be generated, identifying technological opportunities via link prediction (Yoon and Magee 2018;Kim, Kim, and Lee 2019). Hot topics can also be identified and monitored over time (Tu and Seng 2012;Yan 2014).…”