Analyzing and Visualizing Emotional Reactions Expressed by Emojis in Location-Based Social Media

Hauthal, Eva; Burghardt, Dirk; Dunkel, Alexander

doi:10.3390/ijgi8030113

Cited by 24 publications

(15 citation statements)

References 48 publications

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“…We removed links in tweets, but kept emojis, since research has proven that emotions within a text can be expressed through the use of emojis. 37 We used the Python Googletrans 2 implementation package to translate tweets from French to English. We removed tweets in which the word COVID or coronavirus occurs simultaneously with the term mental health or depression.…”

Section: Detection Of Depression Signalsmentioning

confidence: 99%

COVID-19: Detecting depression signals during stay-at-home period

Tshimula

Chikhaoui

Wang

2022

Health Informatics J

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The new coronavirus outbreak has been officially declared a global pandemic by the World Health Organization. To grapple with the rapid spread of this ongoing pandemic, most countries have banned indoor and outdoor gatherings and ordered their residents to stay home. Given the developing situation with coronavirus, mental health is an important challenge in our society today. In this paper, we discuss the investigation of social media postings to detect signals relevant to depression. To this end, we utilize topic modeling features and a collection of psycholinguistic and mental-well-being attributes to develop statistical models to characterize and facilitate representation of the more subtle aspects of depression. Furthermore, we predict whether signals relevant to depression are likely to grow significantly as time moves forward. Our best classifier yields F-1 scores as high as 0.8 and surpasses the utilized baseline by a considerable margin, 0.173. In closing, we propose several future research avenues.

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Section: Detection Of Depression Signalsmentioning

confidence: 99%

COVID-19: Detecting depression signals during stay-at-home period

Tshimula

Chikhaoui

Wang

2022

Health Informatics J

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“…Keeping with the spirit various use cases of hashtags, researchers have also studied and opined on a variety of topics related to hashtags, e.g. Hauthal et al ( 2019 ) have used hashtags for mining opinions related to the Brexit referendum. Twitter itself is a platform for political discussions and has been studied widely under Korean, Canadian and other cultural contexts (Choi et al 2014 ; Gruzd and Roy 2014 ; Guerrero-Solé, 2018 ; Highfield 2013 ).…”

Section: State Of the Artmentioning

confidence: 99%

Analyzing the EU Migration Crisis as Reflected on Twitter

Mukherjee

Hauthal

Burghardt

2022

KN J. Cartogr. Geogr. Inf.

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The proliferation of social media has resulted in its extensive use as a valuable source of information for researchers. This paper aims to use Twitter data to analyze and visualize tweets about the migration crisis in the European Union from 2016 to 2021. The paper uses a methodology to structure data for better understanding of complex social media data. The methods and metrics include the facet model of location based social media, the HyperLogLog data structure and novel uses of the metric typicality. The authors have also developed a web based interactive application closely following the methodology used to organize the dataset. Additionally the work also includes maps using spatial typicality which could be utilized for studying spatial phenomenon. The case study selected also provides unique insights and sets a template for working with multi-lingual geo-social media data. The authors believe that these methods and metrics could be reproduced for other case studies and aid in understanding and communication geo-social media data.

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“…We delete hashtags, mentions, numbers, hyperlinks, non-Arabic words, emojis, punctuation marks, and single characters after we apply tokenization. Please note that, in addition to text details, the emojis provide additional sentiment information [17]. For this job, however, the use of emojis for conducting semantic analysis is out of reach.…”

Section: Data Preprocessingmentioning

confidence: 99%

COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

Albahli¹,

Algsham²,

Aeraj³

et al. 2021

Computers, Materials &Amp; Continua

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Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback. The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms. This phenomenon caused a state of panic among people. Different studies were conducted to stop the spread of fake news to help people cope with the situation. In this paper, a semantic analysis of three levels (negative, neutral, and positive) is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown, on basis of a Twitter dataset of 2 months, using Natural Language Processing (NLP) techniques. It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction, then positive sentiments, and lastly, peaks of negative reactions. The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5% neutral, a 31.2% positive, and an 18.3% negative sentiment overall. The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.

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Analyzing and Visualizing Emotional Reactions Expressed by Emojis in Location-Based Social Media

Cited by 24 publications

References 48 publications

COVID-19: Detecting depression signals during stay-at-home period

COVID-19: Detecting depression signals during stay-at-home period

Analyzing the EU Migration Crisis as Reflected on Twitter

COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

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