Sentiment Analysis based Emotion Extraction for COVID-19 Using Crawled Tweets and Global Statistics for Mental Health

Nandal, Neha; Tanwar, Rohit; Pathan, Al-Sakib Khan

doi:10.1016/j.procs.2023.01.075

“…Aplikasi Anotasi Data Penelitian (ADP) memiliki fitur dalam melakukan custom anotasi menggunakan program library Python dengan melakukan input terhadap data model tersebut. Python memiliki kelebihan di mana dapat melakukan pengembangan web, Analisa data, pemrosesan data dalam satu project [5]. Hasil dari penelitian ini berupa aplikasi untuk Pengembangan Sistem Informasi Anotasi Data Penelitian.…”

Section: Pendahuluanunclassified

Sistem Informasi Anotasi Data Penelitian : Pengolahan Data

Supriyanto,

Siahaan

2024

JTITS

0

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Abstrak-Anotasi data merupakan proses penandaan, pengaitan, atau pelabelan suatu data. Pada proses anotasi data dapat dilakukan dengan menggunakan Google Colaboratory. Langkah yang dilakukan pada penggunaan Google Colaboratory, yaitu dengan melakukan input data teks dan membuat implementasi processing dalam format Python. Akan tetapi, penggunaan implementasi tersebut kurang efektif dan memberikan data hanya dalam satu format. Penggunaan Google Colaboratory juga perlu mengimplementasikan program Python setiap pembuatan project. Untuk mengatasi hal tersebut, pembuatan situs web anotasi data merupakan solusi untuk mempermudah pengguna dalam melakukan anotasi data. Situs web anotasi data memiliki fitur berupa penyimpanan database yang berisi program Python yang menjadi implementasi dalam anotasi data tersebut. Program Python tersebut mencakup penggunaan Natural Language Processing (NLP) dalam dilakukan processing data yang akan dianotasi. Melalui implementasi tersebut akan mempermudah peneliti dalam melakukan proses anotasi data. Situs web ini berfokus pada anotasi data menggunakan framework React.js sebagai implementasi frontend dan Flask sebagai implementasi back end. Pada framework React.js memiliki beberapa fitur seperti export data output dengan format yang ditentukan. Selain itu, pada sisi back end juga dilakukan pembuatan API. Program Python disimpan dalam database menggunakan API upload file. Melalui situs web ini diharapkan dapat mempermudah peneliti dalam melakukan proses anotasi data dan melakukan pengolahan data output.

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“…The emotional well-being of post-COVID patients plays a crucial role in their recovery process. The impacts of the pandemic on various aspects of individuals' lives, including financial, social, and physical, have resulted in psychological and mental health issues [9]. Therefore, understanding and addressing the emotional patterns exhibited by post-COVID patients are essential for their overall well-being and successful recovery.…”

Section: Implications Of Emotional Patterns In Postcovid Patients' Re...mentioning

confidence: 99%

“…Therefore, understanding and addressing the emotional patterns exhibited by post-COVID patients are essential for their overall well-being and successful recovery. Emotional well-being encompasses a range of emotions, including fear, anxiety, depression, and hope, which can significantly impact the recovery process [9]. Identifying the predominant emotional patterns experienced by post-COVID patients can provide valuable insights into their mental health status and guide healthcare professionals in tailoring appropriate interventions [4].…”

Section: Implications Of Emotional Patterns In Postcovid Patients' Re...mentioning

confidence: 99%

“…The country has faced numerous challenges in managing the spread of the virus and providing adequate healthcare resources to its population. The physical and mental health effects of COVID-19 have been profound, with many individuals experiencing long-term symptoms and complications [9]. The pandemic has not only affected the physical well-being of individuals but has also had a significant impact on their mental health [10].…”

Section: Introductionmentioning

confidence: 99%

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Emotion-Driven Sentiment Analysis for Post-COVID Patients

-,

-

2023

IJFMR

0

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Social media has become a powerful platform for individuals to express their thoughts, experiences, and emotions. With the advent of the COVID-19 pandemic, social media has witnessed a surge in posts related to the virus and its aftermath. In India, a country severely hit by the pandemic, there is a prevalence of post-COVID patients sharing their experiences on social media. These posts provide valuable insights into the physical and emotional challenges faced by individuals after recovering from COVID-19. This paper aims to explore the prevalence of post-COVID patients on social media in India, the signs and symptoms experienced by these individuals, the correlations between post-COVID sentiment and patient characteristics, and the development of a predictive model for sentiment analysis. The prevalence of post-COVID patients on social media in India is evident through the increased number of posts related to COVID-19 and its aftermath. The surge in COVID-19 tweets was followed by an increased number of posts expressing personal experiences with the virus, indicating a significant presence of post-COVID patients on social media. These posts provide a valuable resource for understanding the challenges faced by individuals after recovering from COVID-19. Post-COVID patients often experience a range of signs and symptoms that can have a significant impact on their physical and mental well-being. Fatigue, shortness of breath, brain fog, chest pain, headache, and other symptoms have been reported as potential long-term effects of COVID-19. Additionally, patients with post-COVID-19 depressive symptoms share psychopathological symptoms, indicating the presence of mental health challenges. These signs and symptoms highlight the need for a comprehensive understanding of the post-COVID experience and the emotional toll it can take on individuals. Predictive modeling plays a crucial role in analyzing sentiment expressed by post-COVID patients on social media. Predictive modeling is a mathematical process that aims to predict future events or outcomes by analyzing relevant historical data. By developing a predictive model for sentiment analysis, researchers can gain valuable insights into the emotional experiences of post-COVID patients and potentially identify patterns or trends. This modeling approach allows for a systematic analysis of the vast amount of data available on social media platforms.

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“…IJRITCC | October 2023, Available @ http://www.ijritcc.org providing adequate healthcare resources to its population. The physical and mental health effects of COVID-19 have been profound, with many individuals experiencing long-term symptoms and complications [9]. The pandemic has not only affected the physical well-being of individuals but has also had a significant impact on their mental health [10].…”

mentioning

confidence: 99%

Sentiment Analysis of Post-Covid Patients on Social Media by Predictive Modeling

S. Roja

2023

IJRITCC

0

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Social media has become a powerful platform for individuals to express their thoughts, experiences, and emotions. With the advent of the COVID-19 pandemic, social media has witnessed a surge in posts related to the virus and its aftermath. In India, a country severely hit by the pandemic, there is a prevalence of post-COVID patients sharing their experiences on social media. These posts provide valuable insights into the physical and emotional challenges faced by individuals after recovering from COVID-19. This paper aims to explore the prevalence of post-COVID patients on social media in India, the signs and symptoms experienced by these individuals, the correlations between post-COVID sentiment and patient characteristics, and the development of a predictive model for sentiment analysis. The prevalence of post-COVID patients on social media in India is evident through the increased number of posts related to COVID-19 and its aftermath. The surge in COVID-19 tweets was followed by an increased number of posts expressing personal experiences with the virus, indicating a significant presence of post-COVID patients on social media. These posts provide a valuable resource for understanding the challenges faced by individuals after recovering from COVID-19. Post-COVID patients often experience a range of signs and symptoms that can have a significant impact on their physical and mental well-being. Fatigue, shortness of breath, brain fog, chest pain, headache, and other symptoms have been reported as potential long-term effects of COVID-19. Additionally, patients with post-COVID-19 depressive symptoms share psychopathological symptoms, indicating the presence of mental health challenges. These signs and symptoms highlight the need for a comprehensive understanding of the post-COVID experience and the emotional toll it can take on individuals. Predictive modeling plays a crucial role in analyzing sentiment expressed by post-COVID patients on social media. Predictive modeling is a mathematical process that aims to predict future events or outcomes by analyzing relevant historical data. By developing a predictive model for sentiment analysis, researchers can gain valuable insights into the emotional experiences of post-COVID patients and potentially identify patterns or trends. This modeling approach allows for a systematic analysis of the vast amount of data available on social media platforms.

show abstract

Sentiment Analysis based Emotion Extraction for COVID-19 Using Crawled Tweets and Global Statistics for Mental Health

Cited by 6 publications

References 21 publications

Sistem Informasi Anotasi Data Penelitian : Pengolahan Data

Sistem Informasi Anotasi Data Penelitian : Pengolahan Data

Emotion-Driven Sentiment Analysis for Post-COVID Patients

Sentiment Analysis of Post-Covid Patients on Social Media by Predictive Modeling

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