Comparing text corpora via topic modelling

Fedor Krasnov, Mikhail Shvartsman and Alexander Dimentov

International Journal of Data Mining, Modelling and Management, 2022, vol. 14, issue 3, 203-216

Abstract: A method is developed for conducting comparative analysis on the content of full text patents collections. Named T4C, the approach is based on topic modelling and machine learning and extends comparative text mining. The idea of T4C was inspired by the possibility of precise topics extracting from a joint collection of texts and following analysing the parts of collection on the topics. The different aspects of meta information of the patents full texts collection are considered. The ownership of a patent in a particular country can be identified with an accuracy of 97.5% by using supervised machine learning. By studying how patents vary with time, those belonging to a specific period can be identified with an accuracy of 85% for a given country. Also developed is a visual representation of the thematic correlation between groups of patents. In terms of the text composition of patent descriptions, Chinese patents differ fundamentally from US patents. T4C method is valid for structured medium-sized collections of texts in English. The experimental results are used to manage the patenting process at GazpromNeft STC.

Keywords: topic modelling; text classification; ARTM; additive regularisation of topic models; PLSA; random forest; comparing text collections. (search for similar items in EconPapers)
Date: 2022
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