Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language

Schmidt, Thomas; Dennerlein, Katrin; Wolff, Christian

doi:10.18653/v1/2021.latechclfl-1.8

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Drus and Khalid's (2019) study undertaken between 2014 and 2019, found that Naïve Bayes and Support Vector Machine (SVM) learning models were frequently used to detect polarity from text documents on sentiment analysis using a machine learning approach. Schmidt & Burghardt (2018), pointed out that sentiment analysis and emotion analysis are often performed using supervised Machine Learning (ML) or lexicon-based approaches. Supervised machine-learning algorithms are used as classifiers with a set of labelled data in order to classify the incoming words or phrases to appropriate sentiment or emotion categories.…”

Section: Sentiment and Emotion Analysismentioning

confidence: 99%

Sentic-Emotion Classifier on eWallet Reviews

Lim,

Khor,

Tan

2023

International Journal of Business Analytics

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Emotion classification using hybrid framework using lexicon and machine learning algorithms have been proven to be more accurate. This research analyses emotions from reviews of a popular eWallet mobile application in Malaysia. The proposed Sentic-Emotion Classifier is evaluated on its performance as it analyses the code-switched reviews crawled that contain formal and informal or out-of-vocab words. The code-switched reviews are mainly made up of words and expressions in English and Malay language models. This research designs, implements, and investigates several novel techniques that have been shown to have reliable and consistent predictive outcomes, and these outcomes are validated with manually annotated reviews so that the proposed classifier can be evaluated objectively. The novel contributions of the Sentic-Emotion Classifier consist of 2-tier sentiment classification, extended emolex framework, and multi-layer discrete emotion hierarchical classes which is hypothesized to be able to yield better accuracy for emotion and intensity prediction for the proposed framework.

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Section: Sentiment and Emotion Analysismentioning

confidence: 99%

Sentic-Emotion Classifier on eWallet Reviews

Lim,

Khor,

Tan

2023

International Journal of Business Analytics

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Exploring Twitter discourse with BERTopic: topic modeling of tweets related to the major German parties during the 2021 German federal election

Hellwig,

Fehle,

Bink

et al. 2024

Int J Speech Technol

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We present a study in the context of computational social science that explores the topics debated in the context of the 2021 German Federal Election by using the topic modeling technique BERTopic. The corpus consists of German language tweets posted by political party accounts of the major German parties, as well as tweets by the general public mentioning the party accounts. We examined the textual content of the tweets but also included the text in images that were posted into the analysis by extracting the text using optical character recognition (OCR). Our results show that the most frequently discussed topics are party-oriented policies (including call-to-action content), climate policy and financial policy, with these topics being discussed in tweets by both, the political party accounts and tweets by accounts mentioning them. In addition, we observed that some topics were discussed consistently throughout the year, such as the COVID-19 pandemic, climate policy or digitization, while other topics, such as the return to power of the Taliban in Afghanistan or Israel were debated to a greater extent at limited time frames during the election year.

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Machine learning in computational literary studies

Hatzel,

Stiemer,

Biemann

et al. 2023

It - Information Technology

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In this article, we provide an overview of machine learning as it is applied in computational literary studies, the field of computational analysis of literary texts and literature related phenomena. We survey a number of scientific publications for the machine learning methodology the scholars used and explain concepts of machine learning and natural language processing while discussing our findings. We establish that besides transformer-based language models, researchers still make frequent use of more traditional, feature-based machine learning approaches; possible reasons for this are to be found in the challenging application of modern methods to the literature domain and in the more transparent nature of traditional approaches. We shed light on how machine learning-based approaches are integrated into a research process, which often proceeds primarily from the non-quantitative, interpretative approaches of non-digital literary studies. Finally, we conclude that the application of large language models in the computational literary studies domain may simplify the application of machine learning methodology going forward, if adequate approaches for the analysis of literary texts are found.

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Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language

Cited by 3 publications

References 27 publications

Sentic-Emotion Classifier on eWallet Reviews

Sentic-Emotion Classifier on eWallet Reviews

Exploring Twitter discourse with BERTopic: topic modeling of tweets related to the major German parties during the 2021 German federal election

Machine learning in computational literary studies

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