Search Results - subject:"Topic Modelling"

Understanding product returns : a systematic literature review using machine learning and bibliometric analysis

Quang Huy Duong; Zhou, Li; Meng, Meng; Truong Van Nguyen; … - In: International journal of production economics 243 (2022), pp. 1-19

Persistent link: https://www.econbiz.de/10013197614

The strategic role of social media in business-to-business contexts

Pardo, Catherine; Pagani, Margherita; Savinien, Jean - In: Industrial marketing management : the international … 101 (2022), pp. 82-97

Persistent link: https://www.econbiz.de/10013197743

Foundations and research clusters in investor attention : evidence from bibliometric and topic modelling analysis

Goodell, John W.; Kumar, Satish; Li, Xiao; Pattnaik, … - In: International review of economics & finance : IREF 82 (2022), pp. 511-529

Persistent link: https://www.econbiz.de/10013545628

Crowd inputs in reward-based and equity-based crowdfunding: a latent Dirichlet allocation approach on their potential for innovation

Crocco, Edoardo; Giacosa, Elisa; Yahiaoui, Dorra; … - In: European Journal of Innovation Management 27 (2022) 7, pp. 2250-2274

Purpose Crowdfunding platforms are important innovations that allow nascent entrepreneurs to gain access to financial resources and crowd inputs to better refine and develop their business idea. The purpose of this paper is to investigate user-generated content (UGC) from both reward-based and...

Persistent link: https://www.econbiz.de/10015343249

Taxonomies : Practical Approaches to Developing and Managing Vocabularies for Digital Information

Lippell, Helen - 2022

Taxonomies is a curated anthology of case studies of taxonomy projects that showcases the incredible breadth and depth of work being done by taxonomists across many different types of sectors and organisations. The book aims to inspire and educate anyone who needs to organise information and data.

Persistent link: https://www.econbiz.de/10014328360

“I need some space!” deciphering space tourism discussions on social media

Gulati, Shruti - In: Global Knowledge, Memory and Communication 72 (2022) 4/5, pp. 424-436

sentiments by many folds. The top emotions include trust and anticipation. The LDA-based Topic modelling identified seven … emotions and polarity as positive and negative. The findings are complemented with a comparison cloud. Second, LDA Topic … modelling using Gibbs Method is used to find ten broad topics that are used for discussions in space tourism tweets. Data …

Persistent link: https://www.econbiz.de/10014744053

Perception of Dark Tourism : Automated Text Analysis of Users Comments a Case Study of the Chernobyl Exclusion Zone

Kleshcheva, Aleksandra - In: Zeitschrift für Tourismuswissenschaft 13 (2021) 2, pp. 191-208

experiences and leave feedback online. Several natural language processing methods, such as topic modelling (LDA) and sentiment … analysis, were applied to extract the primary motivators behind a visit to Chernobyl. Topic modelling results present five main …

Persistent link: https://www.econbiz.de/10014624568

Lexicon of pandemics: a semantic analysis of the Spanish flu and the Covid-19 timeframe terminology

Lanza, Claudia; Folino, Antonietta; Pasceri, Erika; … - In: Journal of Documentation 78 (2021) 4, pp. 933-952

Purpose The aim of this study is a semantic comparative analysis between the current pandemic and the Spanish flu. It is based on a bilingual terminological perspective oriented to evaluate and compare the terms used to describe and communicate the pandemic's issues both to biomedical experts...

Persistent link: https://www.econbiz.de/10014855357

Evaluating the influence of Airbnb listings' descriptions on demand

Janssens, Bram; Bogaert, Matthias; Van den Poel, Dirk - In: International journal of hospitality management 99 (2021), pp. 1-11

Persistent link: https://www.econbiz.de/10013328054

Digitalisierungsmuster der deutschen Wirtschaft : Konzeption einer Taxonomie zur Analyse des digitalisierungsbedingten Wandels

Kieselbach, Bettina - 2021

Persistent link: https://www.econbiz.de/10012821040