Sentiflood: Process model for flood disaster sentiment analysis

Zaki, Ummu Hani' Hair; Ibrahim, Roliana; Halim, Shahliza Abd; Khaidzir, Khairul Anwar Mohamed; Yokoi, Takamasa

doi:10.1109/icbdaa.2017.8284104

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Recently, studies have shown a growing interest in knowing the feelings of users of different social media platforms through machine learning (ML) and lexicon-based algorithms that analyze and categorize what users think across social media platforms, such as Facebook, Twitter, and YouTube. The analysis of such data helps in understanding the thoughts of individuals and society in response to specific events and predicting future trends, such as those related to the COVID-19 pandemic ( Mittal, Mittal & Aggarwal, 2021 ), tourism ( Chaabani, Toujani & Akaichi, 2018 ), disasters ( Zaki et al, 2018 ), and economy ( Urlam, 2021 ). Despite many publications describing several subjects related to tracking and sentiment analytics in the literature, the Arabic literature on sentiment analysis (SA) topics has been comparatively weak compared with that in English ( Abu Farha & Magdy, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Shambour¹

2022

PeerJ Computer Science

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Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020–2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1–13, 1442 (July 11–23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people’s impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.

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Section: Introductionmentioning

confidence: 99%

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Shambour¹

2022

PeerJ Computer Science

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“…and health care (Denecke and Deng, 2015; Ramamoorthy et al, 2021). In recent years, researchers have extended SA techniques to the emergency and disaster domain to study the sentiments and emotions of people during natural disasters (Alfarrarjeh et al, 2018; Dong et al, 2013; Karmegam and Mappillairaju, 2020; Matsumura et al, 2016; Zaki et al, 2017). This helps in planning the response and recovery operations (Ragini et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Information extraction using a mixed method analysis of social media data: A case study of the police shooting during the anti-Sterlite protests at Thoothukudi, India

Karmegam

Mappillairaju

2021

Information Development

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During unexpected social events, information extracted from social media content posted by the people could play a crucial role in understanding the public opinion about the event. In this study, a mixed method procedure, which combines automated and human-based methods, is proposed to mine information from tweets to understand people's thoughts toward an unexpected turn of events. The proposed framework was applied on tweets posted regarding the police shooting to disperse protesters during the anti-Sterlite protests on May 22, 2018, at Thoothukudi in the Indian state of Tamil Nadu. The tweets were analyzed in two ways: (i) sentiment classification with automated computational methods and (ii) qualitatively examining the context of the expressed sentiments. In the case of anti-Sterlite protests, people expressed mixed emotions toward the protests for the closure of the Sterlite plant. A large negative sentiment toward the police shooting could be gleaned from the tweets. Analyzing tweets by the proposed method provides clear insights regarding the incident, which in turn will aid in planning an emergency response.

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Role of sentiment analysis in social media security and analytics

Sharma

Jain

2020

WIREs Data Min & Knowl

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Social media, in recent times, has with eased an explosion of data with so many social media platforms available to interact and express opinions freely. This has led to easy access to the privacy of social media users which raise broader security concerns and issues. The present paper provides an overview of various sentiment analysis approaches and techniques for social media security and analytics. The multiple security application domains like deception detection, anomaly detection, risk management, and disaster relief have been identified where sentiment analysis is used for social media security. An indepth study on security issues related to data provenance, distrust, e-commerce security, consumer security breaches, market surveillance, credibility, and risk assessment in social media have been presented. A comparison of various techniques, methodologies, dataset, and application domain where sentiment analysis is used has been discussed. The present work discusses the results of different machine learning techniques based on the performance metrics that have been used for the implementation of sentiment analysis in the respective security domains. It identifies the various gaps, issues, and the recent advancements in the field and presents a line of work that needs to be carried forward in future.

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Sentiflood: Process model for flood disaster sentiment analysis

Cited by 7 publications

References 17 publications

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Information extraction using a mixed method analysis of social media data: A case study of the police shooting during the anti-Sterlite protests at Thoothukudi, India

Role of sentiment analysis in social media security and analytics

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