A Novel Approach for Detecting Emotion in Text

Tiwari, Sudhanshu Prakash; Raju, M. V.; Phonsa, Gurbakash; Deepu, Deepak Kumar

doi:10.17485/ijst/2016/v9i29/88211

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…This is normally done by tagging the words of a sentence with Parts-Of-Speech tagger and then extracting the Noun, Verb, Adjective and Adverb (NAVA) words. Most linguistic and emotion based researches mentioned that these are the most probable emotion carrying words [65,66,70]. Then these words are matched against a list of words representing emotions according to a specific emotion model.…”

Section: Keyword-based Methodsmentioning

confidence: 99%

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Emotion and sentiment analysis from Twitter text

Sailunaz

Alhajj

2019

Journal of Computational Science

289

122

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Section: Keyword-based Methodsmentioning

confidence: 99%

“…In some previous works, it was proved that applying a combination of multiple emotion detection methods gives better results than individual methods [54,66].…”

Section: Hybrid Methodsmentioning

confidence: 99%

Emotion and sentiment analysis from Twitter text

Sailunaz

Alhajj

2019

Journal of Computational Science

289

122

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“…Machine learning methods use both supervised [22,23] and unsupervised [24,25] learning for emotion detection, using various existing classification and clustering methods. Hybrid methods merge more than one of the above techniques and apply the results to recognize text emotion [16,[26][27][28]. Emotion is generally defined and described by various emotion models.…”

Section: Social Media Analytics State Of the Artmentioning

confidence: 99%

Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis (Preprint)

Koren¹,

Alam²,

Koneru³

et al. 2021

Preprint

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BACKGROUND Nurses are at the forefront of the COVID 19 pandemic. During the pandemic nurses faced an elevated risk of exposure and experienced hazards related to a novel virus. While being heralded as lifesaving heroes on the front lines of the pandemic, nurses experienced more physical, mental and psychosocial problems as a consequence of the COVID-19 outbreak. Social media discussions by nursing professionals participating in publicly formed Facebook groups is a valuable resource that offers longitudinal insights. OBJECTIVE This study aimed to explore how COVID-19 impacted nurses through capturing public sentiments expressed by nurses on social media discussion platforms and how the sentiments changed over time. METHODS We collected over 110,993 Facebook discussion posts and comments in an open COVID 19 group for nurses from March 2020 until the end of November 2020. A deidentified offline HTML scraping on social media posts and comments was performed. Using subject-matter expert opinions and social media analytics (topic modeling, information retrieval and sentiment analysis), we performed a human-in-a-loop nursing key perspectives analysis to understand the trends of the COVID-19 impact among at risk nursing communities. We further investigated the key insights of the nursing perspectives trends by reporting the temporal changes of emotional effects, frustration reasoning, impacts of isolation, shortage of safety equipment and the frequency of safety equipment uses. Anonymous quotes were highlighted to add context to the data. RESULTS We identified that COVID-19 impacted nurses’ physical, mental and psychosocial health expressed in the form of emotional distress, anger, anxiety, frustration, loneliness and isolation. Major topics discussed were related to work during a pandemic, misinformation from media, improper PPE, PPE side effects, testing positive and loss days of work related to illness. CONCLUSIONS Public Facebook nursing groups are venues for nurses to express their experience, opinions and concerns and can offer researchers an important insight to understand the COVID-19 impact on health care workers.

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Lexicon and Heuristics Based Approach for Identification of Emotion in Text

Akram

Tahir

2018

2018 International Conference on Frontiers of Information Technology (FIT)

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A Novel Approach for Detecting Emotion in Text

Cited by 6 publications

References 12 publications

Emotion and sentiment analysis from Twitter text

Emotion and sentiment analysis from Twitter text

Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis (Preprint)

Lexicon and Heuristics Based Approach for Identification of Emotion in Text

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