Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams

Weichselbraun, Albert; Scharl, Arno; Gindl, Stefan

doi:10.1109/hicss.2016.133

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Our research fits within the literature that investigates environmental and climate change issues using sentiment analysis (see inter alia, Codyetal2015; Hodson, Dale, and Petersen 2018;Scharl et al 2015;Sluban et al 2014Sluban et al , 2015Weichselbraun, Scharl, and Gindl 2016). Our contribution consists of testing the impacts of sentiments derived from the social network content (in particular, Twitter 2 ) related to COVID-19 on the S&P 500 Environmental and Socially Responsible Index under different time-horizons (short-, medium-and long-run).…”

Section: Introductionmentioning

confidence: 77%

“…Several papers have conducted sentiment analysis or opinion mining that are employed to effectively test how positive are changes in the environment (for instance, Scharl et al 2015;Weichselbraun, Scharl, and Gindl 2016) and climate change (for example, Fernandez et al 2016;Hodson, Dale, and Petersen 2018). The majority of these studies focused on examining communications ranging from patterns to its causes and consequences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Is COVID-19 Related Anxiety an Accelerator for Responsible and Sustainable Investing ? A Sentiment Analysis

Selmi¹,

Hammoudeh

Errami³

et al. 2020

Applied Economics

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The excessive volatility generated by the COVID-19 pandemic highlights that environmental and social issues are potential elements that businesses and governments must manage effectively and swiftly. This study seeks to test whether the rising anxiety over this pandemic has affected the attitudes and choices towards environmentally and socially responsible investing. To this end, we first use machine learning tools to examine tweets related to this unprecedented and wild shock. Second, we compare the impact of these sentiments on the stock performance of companies from the S&P500 that meet environmental and social sustainability criteria for three COVID-19 phases with varying levels of anxiety, which we label incubation, fever and the increasing risk of second wave pandemic (in the absence of vaccine). Our findings reveal that the increasing uncertainty and worries over COVID-19 and its consequences has not distracted investors' attention away from environmental and social issues, but companies with responsible strategies on environmental issues that specifically address climate responsibility are likely to be more responsive to sentiments at the current situation of emergency.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Is COVID-19 Related Anxiety an Accelerator for Responsible and Sustainable Investing ? A Sentiment Analysis

Selmi¹,

Hammoudeh

Errami³

et al. 2020

Applied Economics

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“…Consequently, we deployed named entity linking for identifying stakeholders (Section 7.1), the phrase extraction method described by Weichselbraun et al [38] for tracking associations and dominant issues, and sentiment analysis (Section 7.2) to automatically determine whether the feedback was positive or negative.…”

Section: Knowledge Extractionmentioning

confidence: 99%

Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities

et al. 2021

Self Cite

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Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.

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“…The Integrity Risks Monitor draws upon documents obtained from popular Austrian, German, Swiss, U.K. and U.S. media outlets that are pre-processed, analyzed and semantically enriched using methods such as part-of-speech tagging, dependency parsing [21], sentiment analysis [18], keyword analysis [20] and named entity linking [19]. In addition, we assign a score to each document that indicates its likelihood to contain coverage on integrity risks.…”

Section: Identifying Media Coverage On Integrity Risksmentioning

confidence: 99%

Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web Intelligence

Weichselbraun

Hörler

Hauser

et al. 2020

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics

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A substantial number of international corporations have been affected by corruption. The research presented in this paper introduces the Integrity Risks Monitor, an analytics dashboard that applies Web Intelligence and Deep Learning to english and germanspeaking documents for the task of (i) tracking and visualizing past corruption management gaps and their respective impacts, (ii) understanding present and past integrity issues, (iii) supporting companies in analyzing news media for identifying and mitigating integrity risks.Afterwards, we discuss the design, implementation, training and evaluation of classification components capable of identifying English documents covering the integrity topic of corruption. Domain experts created a gold standard dataset compiled from Anglo-American media coverage on corruption cases that has been used for training and evaluating the classifier. The experiments performed to evaluate the classifiers draw upon popular algorithms used for text classification such as Naïve Bayes, Support Vector Machines (SVM) and Deep Learning architectures (LSTM, BiLSTM, CNN) that draw upon different word embeddings and document representations. They also demonstrate that although classical machine learning approaches such as Naïve Bayes struggle with the diversity of the media coverage on corruption, state-of-the art Deep Learning models perform sufficiently well in the project's context. CCS CONCEPTS• Information systems → Data analytics; • Computing methodologies → Neural networks; • Applied computing → Economics; Annotation.

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Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams

Cited by 5 publications

References 15 publications

Is COVID-19 Related Anxiety an Accelerator for Responsible and Sustainable Investing ? A Sentiment Analysis

Is COVID-19 Related Anxiety an Accelerator for Responsible and Sustainable Investing ? A Sentiment Analysis

Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities

Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web Intelligence

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