Comparing Attitudes to Climate Change in the Media using sentiment
            analysis based on Latent Dirichlet Allocation

Jiang, Ye; Song, Xingyi; Harrison, Jackie; Quegan, S.; Maynard, Diana

doi:10.18653/v1/w17-4205

Cited by 16 publications

(11 citation statements)

References 20 publications

(16 reference statements)

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“…Results are plotted in particular for correlations of emotions and topics and the role of "influencers" versus less prominent accounts. Jiang et al (2017) gathered 11,000 newspaper articles from four British broadsheets over the years 2007-2016. The search criterion was that cc has to occur at least three times.…”

Section: Discourse: Research In the Nlp Communitymentioning

confidence: 99%

“…Summary In the absence of any "standard CC dataset", the NLP research so far has been scattered. Types of target texts (Discourse 1 ) were limited to news (Jiang et al, 2017;Luo et al, 2020), blogs Salway et al, 2016) and Twitter (Pathak et al, 2017;Koenecke and Feliu-Fabà, 2020); no comparisons across genres or channels were made, and there was no attention on political arenas or on statements by individuals and interest groups that are meant to directly influence policy-making. In terms of methods and goals we found network analysis for detecting communities (Salway et al, 2016;Pathak et al, 2017), sentiment/stance classification for Discourse 2 grouping Pathak et al, 2017;Jiang et al, 2017;Luo et al, 2020;Koenecke and Feliu-Fabà, 2020), topic modeling for computing topic/sentiment correlations (Jiang et al, 2017), and fine-grained framing distinction (Luo et al, 2020).…”

Section: Discourse: Research In the Nlp Communitymentioning

confidence: 99%

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The Climate Change Debate and Natural Language Processing

Stede¹,

Patz²

2021

Proceedings of the 1st Workshop on NLP for Positive Impact

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The debate around climate change (CC)-its extent, its causes, and the necessary responses-is intense and of global importance. Yet, in the natural language processing (NLP) community, this domain has so far received little attention. In contrast, it is of enormous prominence in various social science disciplines, and some of that work follows the "text-as-data" paradigm, seeking to employ quantitative methods for analyzing large amounts of CC-related text. Other research is qualitative in nature and studies details, nuances, actors, and motivations within CC discourses. Coming from both NLP and Political Science, and reviewing key works in both disciplines, we discuss how social science approaches to CC debates can inform advances in text-mining/NLP, and how, in return, NLP can support policy-makers and activists in making sense of large-scale and complex CC discourses across multiple genres, channels, topics, and communities. This is paramount for their ability to make rapid and meaningful impact on the discourse, and for shaping the necessary policy change.

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Section: Discourse: Research In the Nlp Communitymentioning

confidence: 99%

Section: Discourse: Research In the Nlp Communitymentioning

confidence: 99%

The Climate Change Debate and Natural Language Processing

Stede¹,

Patz²

2021

Proceedings of the 1st Workshop on NLP for Positive Impact

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“…However, there has been limited application of sentiment analysis techniques for analyzing opinions about climate change policies in mass and social media. For example, (Jiang et al, 2017) assess the public perception and sentiments regarding climate change and adaptive and mitigating measures like renewable energy. Dahal et al (2019) and Loureiro and Alló (2020) have employed sentiment analysis techniques for analyzing tweets on climate change.…”

Section: Media Content Analysis For Climate Change Via Topic Modelingmentioning

confidence: 99%

“…Print media and social media texts have also been analyzed extensively in the NLP community via a number of tasks like opinion mining (Ravi and Ravi, 2015), controversy detection Dori-Hacohen, 2015), fake news detection (Zhou and Zafarani, 2020;Thorne et al, 2017), argument mining (Lippi and Torroni, 2016), stance detection (Ghosh et al, 2019;Küc ¸ük and Can, 2020), perspective identification (Wong et al, 2016), inter alia. Researchers have recently started exploring media's perception on climate issues (Jiang et al, 2017;Luo et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action

Swarnakar¹,

Modi²

2021

Preprint

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Climate change is a burning issue of our time, with the Sustainable Development Goal (SDG) 13 of the United Nations demanding global climate action. The worldwide scientific evidence exhibits that the current processes of market-led economic development are unsustainable. Realizing the urgency, in 2015 in Paris, world leaders signed an agreement committing to taking voluntary action to reduce carbon emissions. However, the scale, magnitude, and climate action processes vary globally, especially between developed and developing countries. Therefore, from parliament to social media, the debates and discussions on climate change gather data from wide-ranging sources essential to the policy design and implementation. The downside is that we do not currently have the mechanisms to pool the worldwide dispersed knowledge emerging from the structured and unstructured data sources.The paper thematically discusses how NLP techniques could be employed in climate policy research and contribute to society's good at large. In particular, we exemplify symbiosis of NLP and Climate Policy Research via four methodologies. The first one deals with the major topics related to climate policy using automated content analysis. We investigate the opinions (sentiments) of major actors' narratives towards climate policy in the second methodology. The third technique explores the climate actors' beliefs towards pro or anticlimate orientation. Finally, we discuss developing a 'Climate Knowledge Graph.'The present theme paper further argues that creating a knowledge platform would help in the formulation of a holistic climate policy and effective climate action. Such a knowledge platform would integrate the policy actors' varied opinions from different social sectors like government, business, civil society, and the scientific community. The research outcome will add value to effective climate action because policymakers can make informed decisions by looking at the diverse public opinion on a comprehensive platform.

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“…Studies have shown that data from Twitter are relevant to climate events and valuable for measuring opinion polarization [9], [12], [13]. Due to the absence of large annotated datasets in different climate change-related topics, most previous works took an unsupervised learning fashion [14], [15] to classify tweets into topic categories. These methods use topic models to obtain extractive topic words, then map the topic words to major topic categories by the semantics of the topic words.…”

Section: Introductionmentioning

confidence: 99%

Saving Earth One Tweet at a Time through the Lens of Artificial Intelligence

Duong

Liu

Mao

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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The impacts of climate change and global warming have become more visible globally because of the increasing frequency of extreme weather events, abnormal heatwaves, and other climate crises. Besides the traditional survey method, it is benecial to automatically distillate climate change opinions from social platforms to measure public reactions quickly. We investigate how to organize climate change opinions on Twitter into meaningful categories to support perspective summarizing tasks. We nd that merely using the available taxonomy for this task is ineffective; hence we must consider the entire text content. We recommend ve high-level categories (Root cause, Impact, Mitigation, Politics or Policy, Others) and assemble ClimateTweets, a dataset with category and polarity labels. In addition, we construct category classication and polarity detection tasks with a range of opinion mining baselines. The experimental results show that both tasks are challenging for existing models. We release the ClimateTweets dataset to facilitate investigation in public opinion mining using text content and articial intelligent methods. We hope this study could pave the way for future studies in the climate change domain.

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Comparing Attitudes to Climate Change in the Media using sentiment analysis based on Latent Dirichlet Allocation

Cited by 16 publications

References 20 publications

The Climate Change Debate and Natural Language Processing

The Climate Change Debate and Natural Language Processing

NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action

Saving Earth One Tweet at a Time through the Lens of Artificial Intelligence

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