Title: Sentiment analysis of Danish healthcare industries' financial text

Authors: Rudra Pratap Deb Nath; Emil Bækdahl; Magnus Brogaard Larsen; Jakob Skallebæk; Jesper Juul Severinsen

Addresses: Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh ' Department of Computer Science, Aalborg University, Aalborg, Denmark ' Department of Computer Science, Aalborg University, Aalborg, Denmark ' Department of Computer Science, Aalborg University, Aalborg, Denmark ' Department of Computer Science, Aalborg University, Aalborg, Denmark

Abstract: Sentiment analysis enables organisations to gain insights into market trends and customer opinions expressed in textual format. It quantifies textual opinions by classifying them as positive, negative, or neutral. We present a system for performing sentiment analysis on Danish texts related to the Danish healthcare industry. The system is composed of two components: domain-specific sentiment lexicon (DSSL) generator and dependency tree-based sentence analyser (DTSA). To generate DSSL, we use company stock prices to automatically label the sentiments of financial news articles based on the point-wise mutual information method and achieve performance improvements compared to existing general sentiment lexicons. Our DTSA is based on a data structure called a dependency tree, which describes how words in a text are connected. Depending on the types of connections between the words, we apply different rules to compute a sentiment value. This approach, in conjunction with DSSL, performs best in three-class sentence classification compared to systems using different sentiment lexicons and/or sentiment analysis components. We achieve an accuracy of 53% and the best F1 scores.

Keywords: sentiment analysis; Danish text mining; business intelligence; knowledge discovery; natural language processing; extract-tansform-load; ETL.

DOI: 10.1504/IJDMMM.2025.150985

International Journal of Data Mining, Modelling and Management, 2025 Vol.17 No.4, pp.450 - 479

Received: 24 Feb 2024
Accepted: 18 Aug 2024
Published online: 07 Jan 2026 *

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