Emotion Analysis of Twitter Data Using Hashtag Emotions

Goel, Prerna; Thareja, Reema

doi:10.1007/978-981-13-2035-4_9

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Using RoBERTa and the Covid 19 context, our study also offered more emotions than the existing emotions that were initially found in archives by researchers such as Jain et al (2017) or Goel and Thareja (2018). The emotions that they used for their studies were Happiness, Sadness, Anger, Disgust, Surprise, Fear, Tired, Afraid, Sleepy, Relaxed, Bored, Excited.…”

Section: Discussion Of This Studymentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

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Section: Discussion Of This Studymentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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“…Stieglitz et al [32] studied the relationship between emotion and information dissemination in social media and found that emotional Twitter messages were forwarded more frequently and faster compared to neutral ones. Goel and Thareja [40], based on Twitter data, analyzed user sentiment and found that fear is the least expressed and sadness is the most expressed emotion. Fan, et al [41] explored the emotional associations of users in Weibo.…”

Section: Emotional Analysismentioning

confidence: 99%

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

Gao

et al. 2020

Sustainability

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(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

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Emotion Analysis of Twitter Data Using Hashtag Emotions

Cited by 2 publications

References 4 publications

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

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