Strategic sentiments and emotions in post-Second World War party manifestos in Finland

Koljonen, Juha; Öhman, Emily; Ahonen, Pertti; Mattila, Mikko

doi:10.1007/s42001-022-00181-9

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…This phenomenon is well documented with several lexicon-based packages, such as R's Syuzhet [Jockers, 2017], which prioritizes word frequency to its analysis (leading to misclassification of sentiments to text) alongside their limited ability to detect negation. Nonetheless, studies have also shown that lexicon-based methods can still be a valid tool for analyzing the emotions and sentiments within texts.…”

Section: Methods 1: Vader Sentiment Analysismentioning

confidence: 92%

The Impact of Incumbent/Opposition Status and Ideological Similitude on Emotions in Political Manifestos

Nishi

2023

Journal of Data Mining &Amp; Digital Humanities

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The study involved the analysis of emotion-associated language in the UK Conservative and Labour party general election manifestos between 2000 to 2019. While previous research have shown a general correlation between ideological positioning and overlap of public policies, there are still conflicting results in matters of sentiments in such manifestos. Using new data, we present how valence level can be swayed by party status within government with incumbent parties presenting a higher frequency in positive emotion-associated words while negative emotion-associated words are more prevalent in opposition parties. We also demonstrate that parties with ideological similitude use positive language prominently further adding to the literature on the relationship between sentiments and party status.

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Section: Methods 1: Vader Sentiment Analysismentioning

confidence: 92%

The Impact of Incumbent/Opposition Status and Ideological Similitude on Emotions in Political Manifestos

Nishi

2023

Journal of Data Mining &Amp; Digital Humanities

View full text Add to dashboard Cite

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"Approaches to sentiment analysis of Hungarian political news at the sentence level"

Ring,

Szabó,

Guba

et al. 2024

Lang Resources & Evaluation

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Automated sentiment analysis of textual data is one of the central and most challenging tasks in political communication studies. However, the toolkits available are primarily for English texts and require contextual adaptation to produce valid results—especially concerning morphologically rich languages such as Hungarian. This study introduces (1) a new sentiment and emotion annotation framework that uses inductive approaches to identify emotions in the corpus and aggregate these emotions into positive, negative, and mixed sentiment categories, (2) a manually annotated sentiment data set with 5700 political news sentences, (3) a new Hungarian sentiment dictionary for political text analysis created via word embeddings, whose performance was compared with other available sentiment dictionaries. (4) Because of the limitations of sentiment analysis using dictionaries we have also applied various machine learning algorithms to analyze our dataset, (5) Last but not least to move towards state-of-the-art approaches, we have fine-tuned the Hungarian BERT-base model for sentiment analysis. Meanwhile, we have also tested how different pre-processing steps could affect the performance of machine-learning algorithms in the case of Hungarian texts.

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The geopolitics of vaccine media representation in Orbán’s Hungary—an AI-supported sentiment analysis

Sebők,

Ring,

Kis

et al. 2024

J Comput Soc Sc

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Extant studies on the European media coverage of the COVID-19 pandemic generally posit a linear relationship between the severity of the public health emergency and the volume of media reports. However, domestic politics and geopolitics may also impact the saliency, distribution, and sentiment of coverage in different outlets. Under Viktor Orbán’s illiberal leadership, Hungary sought deals for ventilators and vaccines from China and Russia—a deviation from joint European procurements. In this article, we conduct a content analysis of pro-government and Orbán-critical media to examine differences in their treatment of Eastern and Western vaccines. We relied on state-of-the-art deep learning analysis (a branch of articifial intelligence) to investigate all COVID-19-related articles (N = 72,339) published on three major Hungarian news portals between March 2020 and March 2022. We used a new fine-tuned BERT model for emotion analysis, the categories of which have been aggregated into three sentiment labels (positive, negative, and neutral). Our sentiment analysis results show a positive correlation between the number of sentences mentioning at least one of the vaccines and the (first) shots administered for only one outlet out of three. The pro-government portal in the sample showed more positivity towards Western vaccines than a hard-right, anti-government one. This latter also produced more positive reports concerning the Russian vaccine. These results shed light on the complex geopolitics of vaccine narratives in Hungarian media. Our research contributes not only to our understanding of illiberal media systems but also by sharing a new public dataset and a fine-tuned large language model that is applicable to alternative research questions and designs.

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Strategic sentiments and emotions in post-Second World War party manifestos in Finland

Cited by 3 publications

References 29 publications

The Impact of Incumbent/Opposition Status and Ideological Similitude on Emotions in Political Manifestos

The Impact of Incumbent/Opposition Status and Ideological Similitude on Emotions in Political Manifestos

"Approaches to sentiment analysis of Hungarian political news at the sentence level"

The geopolitics of vaccine media representation in Orbán’s Hungary—an AI-supported sentiment analysis

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