Do There Exist an Emotion Trend in Scientific Papers? PRO-VE Conference as a Case
Authors: Venumuddala, R., Xu, L. and de Vrieze, P.
Journal: IFIP Advances in Information and Communication Technology
Volume: 726 IFIP
Pages: 118-133
eISSN: 1868-422X
ISSN: 1868-4238
DOI: 10.1007/978-3-031-71739-0_8
Abstract:Scientific writing aims for formality and objectivity, yet emotions are integral to human communication, decision-making, and collaboration, all of which are fundamental to scientific progress. Existing research on emotion detection has mainly focused on datasets from social media and online platforms, where emotional expressions are abundant. However, scientific texts pose unique challenges due to their formal language and the rarity of explicit emotional words, necessitating specialised investigation. This study investigates the presence and nature of emotions in scientific texts, specifically analysing the abstracts from the PRO-VE conference series from 2012 to 2022. Two emotion detection methods are employed: a lexicon-based approach and a hybrid machine learning-based approach. The lexicon-based approach utilises the NRC Emotion Lexicon to identify and quantify emotions within the PRO-VE abstracts, while the hybrid approach integrates Word2Vec for word embedding generation and a Random Forest classifier for emotion prediction. The findings reveal a predominance of positive emotions, such as trust, anticipation, and joy, in the PRO-VE abstracts, consistent with the objective nature of scientific writing. In light of the PRO-VE conference series’ 25th anniversary, an analysis of trends and patterns in the detected emotions offers insights into the emotional landscape of this prestigious conference series. The study also critically examines the limitations of the experiments, including the dataset size and the prevalence of positive emotions.
https://eprints.bournemouth.ac.uk/40332/
Source: Scopus
Do There Exist an Emotion Trend in Scientific Papers? PRO-VE Conference as a Case
Authors: Venumuddala, R., Xu, L. and de Vrieze, P.
Journal: NAVIGATING UNPREDICTABILITY: COLLABORATIVE NETWORKS IN NON-LINEAR WORLDS, PRO-VE 2024, PT I
Volume: 726
Pages: 118-133
eISSN: 1868-422X
ISBN: 978-3-031-71741-3
ISSN: 1868-4238
DOI: 10.1007/978-3-031-71739-0_8
https://eprints.bournemouth.ac.uk/40332/
Source: Web of Science (Lite)
Do There Exist an Emotion Trend in Scientific Papers? PRO-VE Conference as a Case
Authors: Xu, L. and de Vrieze, P.
Conference: 25th FIFP/SOCOLNET working Conference on Virtual Enterprises (PRO-VE 2024)
Dates: 28-30 October 2024
Publisher: Springer
Abstract:Scientific writing aims for formality and objectivity, yet emotions are integral to human communication, decision-making, and collaboration, all of which are fundamental to scientific progress. Existing research on emotion detection has mainly focused on datasets from social media and online platforms, where emotional expressions are abundant. However, scientific texts pose unique challenges due to their formal language and the rarity of explicit emotional words, necessitating specialised investigation. This study investigates the presence and nature of emotions in scientific texts, specifically analysing the abstracts from the PRO-VE conference series from 2012 to 2022. Two emotion detection methods are employed: a lexicon-based approach and a hybrid machine learning-based approach. The lexicon-based approach utilises the NRC Emotion Lexicon to identify and quantify emotions within the PRO-VE abstracts, while the hybrid approach integrates Word2Vec for word embedding generation and a Random Forest classifier for emotion prediction. The findings reveal a predominance of positive emotions, such as trust, anticipation, and joy, in the PRO-VE abstracts, consistent with the objective nature of scientific writing. In light of the PRO-VE conference series' 25th anniversary, an analysis of trends and patterns in the detected emotions offers insights into the emotional landscape of this prestigious conference series. The study also critically examines the limitations of the experiments, including the dataset size and the prevalence of positive emotions.
https://eprints.bournemouth.ac.uk/40332/
Source: Manual
Do There Exist an Emotion Trend in Scientific Papers? PRO-VE Conference as a Case
Authors: Venumuddala,, R., Xu, L. and de Vrieze, P.
Editors: Camarinha-Matos, L.M., Ortiz, A., Boucher, X. and Barthe-Delanoë, A.-M.
Volume: 726
Pages: 118-133
Publisher: Springer
Place of Publication: Cham
ISBN: 9783031717383
Abstract:Scientific writing aims for formality and objectivity, yet emotions are integral to human communication, decision-making, and collaboration, all of which are fundamental to scientific progress. Existing research on emotion detection has mainly focused on datasets from social media and online platforms, where emotional expressions are abundant. However, scientific texts pose unique challenges due to their formal language and the rarity of explicit emotional words, necessitating specialised investigation. This study investigates the presence and nature of emotions in scientific texts, specifically analysing the abstracts from the PRO-VE conference series from 2012 to 2022. Two emotion detection methods are employed: a lexicon-based approach and a hybrid machine learning-based approach. The lexicon-based approach utilises the NRC Emotion Lexicon to identify and quantify emotions within the PRO-VE abstracts, while the hybrid approach integrates Word2Vec for word embedding generation and a Random Forest classifier for emotion prediction. The findings reveal a predominance of positive emotions, such as trust, anticipation, and joy, in the PRO-VE abstracts, consistent with the objective nature of scientific writing. In light of the PRO-VE conference series' 25th anniversary, an analysis of trends and patterns in the detected emotions offers insights into the emotional landscape of this prestigious conference series. The study also critically examines the limitations of the experiments, including the dataset size and the prevalence of positive emotions.
https://eprints.bournemouth.ac.uk/40332/
Source: BURO EPrints