Analyzing the depression and suicidal tendencies of people affected by COVID-19’s lockdown using sentiment analysis on social networking websites

Sharma, Sparsh; Sharma, Surbhi

doi:10.1080/09720510.2020.1833453

Cited by 17 publications

(7 citation statements)

References 13 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…on one hand, emotion analysis and emotion calculation methods can provide effective tools for objective evaluation and monitoring of psychological disorders, especially depression (Zucco et al, 2017 ; Sharma & Sharma, 2021 ). Especially affected by covid-19, blockade resulted in higher depression and suicidal tendency.…”

Section: Discussionmentioning

confidence: 99%

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

et al. 2022

View full text Add to dashboard Cite

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

show abstract

Section: Discussionmentioning

confidence: 99%

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Most of these topics were associated with healthcare, case reports and daily statistics. Sharma and Sharma [35] Performed sentiment analysis to understand the online behaviors of people while they were locked inside their homes. They found that many of these people developed suicidal tendencies and boredom, necessitating an analysis of their psychology and sentiments to stop them from taking extreme decisions, such as their lives.…”

Section: Related Workmentioning

confidence: 99%

Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

2022

KSII TIIS

View full text Add to dashboard Cite

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown 2170 Alamoodi et al.: Public Sentiment Analysis and Topic Modeling Regarding COVID-19's Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysiaas reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

show abstract

“…Ye, Özbay, Zuo ve Chen (2021), yine korona virüs salgını sırasında insanların seyahat ile ilgili tweetlerinin seyahat davranışları üzerindeki etkilerini New York şehri örneğinden API'ler aracılığıyla 13 Ocak ile 31 Ağustos 2020 tarihleri arasında toplanan verilerle incelemişler, sonuç olarak New York şehrinde salgın esnasında Twitter gönderilerinin zaman serisi ile seyahat oranı arasında benzer bir eğilim olduğunu tespit etmişlerdir. Bir başka çalışmada Sharma ve Sharma (2021), Twitter'da duygu analizi için COVID-19'un getirdiği kısıtlama ve karantina süreçlerinden etkilenen insanların depresyon ve intihar eğilimlerini araştırmıştır. 15 Şubat ile 19 Mayıs 2020 tarih aralığındaki tweetler Twitter API'si ile toplanıp saklanarak analiz edilmiştir.…”

Section: Salgınlarunclassified

Bir Araştırma Kaynağı Olarak Arşivlenen Sosyal Medya Verilerinin Kullanımı

Kaya¹

2021

bba

View full text Add to dashboard Cite

Günümüzde sosyal medya, bireysel olarak kullanımının yanında devlet kurumlarından medya kuruluşlarına, özel sektörden akademik araştırmalara kadar bir dizi alanda kullanılmaktadır. Sosyal medyanın bu çok sayıda alanda kullanımı ile birlikte, araştırmacıların çalışmalarında kullanabilecekleri değerli yeni veriler ortaya çıkmaktadır. "Sosyal medya verileri" olarak adlandırılan bu değerli veri türü daha önce görülmemiş bir nitelikte bir araştırma kaynağı yaratmıştır. Uygulama Programlama Arayüzlerinin (Application Programming Interface, API) ortaya çıkması ve sosyal medya platformlarının API'ler etrafında şekillenmesi, araştırmacıları sosyal medya platformlarından veri toplamanın ve verileri arşivlemenin en etkili yollarından biri olarak platform API'lerini kullanmaya yöneltmiştir. Bilgi ve belge yönetimi alanında değeri yeni fark edilen sosyal medya verileri üzerine pek fazla çalışma bulunmamaktadır. Bu çalışmada; sosyal medya verileri kavramı ile sosyal medya platformlarının API'leri aracılığıyla toplanıp arşivlenen sosyal medya verilerinin araştırmalarda yeni bir kaynak türü olarak önemine vurgu yapılmış, bu verilerin hâlihazırda kullanıldığı akademik araştırma alanlarına işaret edilmiştir. Çalışma kapsamında ayrıca sosyal medya büyük verisi ve sosyal medya veri madenciliği kavramları da açıklanmıştır. Literatüre dayalı nitel araştırma yönteminin kullanıldığı çalışmada elde edilen verilerin analizi için de doküman analizi tekniğinden yararlanılmıştır.

show abstract

Analyzing the depression and suicidal tendencies of people affected by COVID-19’s lockdown using sentiment analysis on social networking websites

Cited by 17 publications

References 13 publications

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

Bir Araştırma Kaynağı Olarak Arşivlenen Sosyal Medya Verilerinin Kullanımı

Contact Info

Product

Resources

About