ASEDS: Towards Automatic Social Emotion Detection System Using Facebook Reactions

Raad, Bin Tareaf; Berger, Philipp; Hennig, Patrick; Meinel, Christoph

doi:10.1109/hpcc/smartcity/dss.2018.00143

Cited by 20 publications

(12 citation statements)

References 8 publications

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“…Crawlers such as RBSE Spider [6], Mercator [7], UbiCrawler [8], BlogForever crawler [9], GitcProc [10] were developed for diverse domains such as search engines, blogs, GitHub commits, etc. Several studies have been done on the development of focused crawlers for corpus generation in different domains, for example [11][12][13][14][15]. Ref.…”

Section: Background and Motivationmentioning

confidence: 99%

“…[14] developed a Facebook website crawler to retrieve comments from Facebook posts, which was able to collect 7,567 comments. The Automatic Social Emotion Detection System (ASEDS) was engineered in [15] using 3 million posts from 64,000 Facebook pages of different domains, obtained by developing a scalable crawler. In the same line, Ref.…”

Section: Background and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Development of Focused Crawlers for Building Large Punjabi News Corpus

Mahi

Verma

2021

J. ICT Res. Appl.

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Web crawlers are as old as the Internet and are most commonly used by search engines to visit websites and index them into repositories. They are not limited to search engines but are also widely utilized to build corpora in different domains and languages. This study developed a focused set of web crawlers for three Punjabi news websites. The web crawlers were developed to extract quality text articles and add them to a local repository to be used in further research. The crawlers were implemented using the Python programming language and were utilized to construct a corpus of more than 134,000 news articles in nine different news genres. The crawler code and extracted corpora were made publicly available to the scientific community for research purposes.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Development of Focused Crawlers for Building Large Punjabi News Corpus

Mahi

Verma

2021

J. ICT Res. Appl.

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“…Several studies have been conducted to measure and understand emotions using social media data [23][24][25][26][27]. Tian et al [28] studied the way Facebook users modify the sentiment of their comments with emojis.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring the Diversity of Facebook Reactions to Research

Freeman

Alhoori

Shahzad

2020

Proc. ACM Hum.-Comput. Interact.

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Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared-a conclusion that contradicts previous studies of other social media platforms.

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“…This approach allows building a larger dataset by eliminating the need for extensive manual tagging. Some other studies [19][20][21][22][23][24][25][26][27] have already used Facebook reactions but, unlike this work, they linked the reaction to the topic from which it stemmed.…”

Section: Introductionmentioning

confidence: 99%

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

et al. 2021

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Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.

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ASEDS: Towards Automatic Social Emotion Detection System Using Facebook Reactions

Cited by 20 publications

References 8 publications

Development of Focused Crawlers for Building Large Punjabi News Corpus

Development of Focused Crawlers for Building Large Punjabi News Corpus

Measuring the Diversity of Facebook Reactions to Research

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

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