Sentiment Analysis of Floods on Twitter Social Media Using the Naive Bayes Classifier Method with the N-Gram Feature

Ridwan, Akbar; Nuha, Hilal Hudan; Dharayani, Ramanti

doi:10.1109/icodsa55874.2022.9862827

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Furthermore, when considering the years in which the research was conducted, the category of methods most frequently employed over the last two years is Neural Network/Deep Learning, found in 7 literature [8], [9], [10], [13], [18], [20], [22] followed by the Classic category present in 6 literature [8], [10], [11], [12], [13], [22]. The methods used in sentiment analysis found in the 15 literature is illustrated in Table 4.…”

Section: Methodsmentioning

confidence: 99%

“…Out of the 15 analyzed literature, 11 of them [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] utilized the social media platform Twitter for sentiment analysis, 1 literature [19] used Weibo, which is a commonly used social media platform in China, and 2 literature [20], [21] used government websites. Furthermore, there is one literature [22] that utilized three social media platforms, namely Twitter, Reddit, and Youtube.…”

Section: Media Sosialmentioning

confidence: 99%

“…The average number of datasets used in the research is 94,110 datasets, with the smallest dataset comprising 989 datasets in the study [15] and the largest dataset consisting of 795,065 datasets in the study [18]. Additionally, there are three studies that do not mention the number of datasets processed [8], [12], [20].…”

Section: Figure 2 Media Social Utilization 42 Datasetmentioning

confidence: 99%

“…The data collection found in the 15 reviewed literatures can be categorized into three groups, namely data collection techniques, data source/format, and tools. In accordance with the mentioned categories, data collection used are: 1) Technique: Crawling Data by [20], [21]; 2) Data Source/Format: Twitter API by authors [8], [9], [11], [12], [14], [15], [18], Weibo API by authors [19], Tweepy by authors [16], [17], and Reddit API by authors [22]; 3) Tools: Python Library by authors [10], [16], SNScrape by authors [10], and R vosonSML package by authors [22]. Figure 3 displays the data collection alongside the corresponding research counts.…”

Section: Data Collectionmentioning

confidence: 99%

See 3 more Smart Citations

Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

Permana,

Gofur,

Budi

et al. 2024

SISTEMASI

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The need for a healthy and pollution-free environment is the basis of the problem that this study examines. Social media has become an integral aspect of daily existence for the majority engaged in the digital realm. It enables individuals from various backgrounds to utilize these platforms to stay updated on the latest information, such as the current state of pollution in Jakarta. This research explores the attitudes of social media users regarding their perspectives on air pollution in Jakarta. The method used includes conducting a Systematic Literature Review of academic papers released from 2020 to 2023. The results of this research can unveil the types of social media platforms utilized, the quantity of datasets, the procedures for data collection, data preprocessing techniques, and the commonly employed methods in sentiment analysis studies concerning the subject of air pollution.

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Section: Methodsmentioning

confidence: 99%

Section: Media Sosialmentioning

confidence: 99%

Section: Figure 2 Media Social Utilization 42 Datasetmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

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Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

Permana,

Gofur,

Budi

et al. 2024

SISTEMASI

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“…This flood occurs throughout the year from January 2021 to December 2021. The cause of the flood is due to high tides accompanied by high rainfall, so that the overflowing river cannot accommodate water (Hidayatullah, 2021;Ridwan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

PREDICTION, MONITORING, AND EARLY WARNING SYSTEM OF WATER LEVELS IN FLOOD PROGRAM AREAS IN BANJARMASIN USING IoT AND LINEAR REGRESSION

Wicaksana

Nuha

2022

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Banjarmasin is a city of a thousand rivers, floods often occur due to high tides and accompanied by rain. An early warning system is needed for monitoring and predicting river tides, especially in rivers that are prone to disasters. This study aims to develop an early warning system for river water levels using IoT and predicting linear regression analysis. The research method is through the manufacture of the NodeMCU V3 early warning system device , Microcontroller, and Ultrasonic A02YYUW. Monitoring to collect water level data at high tide is carried out on the banks of the river, then predictions are made using simple linear regression analysis. The results showed that an early warning system for river water levels at high tide can be read in the A02YYUW ultrasonic sensor database which is sent to the Mysql database. Water surface distance data with tools are used to predict early warning, simple regression analysis shows significant results on the t test and simple linear regression equation = 29.472 - 0.061 X + e. This means that the river water level at high tide approaches an early warning tool, namely an ultrasonic sensor. The conclusion of this study is that an IoT-based early warning system combined with simple linear regression can monitor and predict river water level rise.

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Deep Learning-Based Disaster Sentiment Analysis of Unbalanced Twitter Data: The Case of Indonesia Earthquake

Amriza,

Wijayanto

2023

2023 11th International Conference on Cyber and IT Service Management (CITSM)

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Sentiment Analysis of Floods on Twitter Social Media Using the Naive Bayes Classifier Method with the N-Gram Feature

Cited by 6 publications

References 7 publications

Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

PREDICTION, MONITORING, AND EARLY WARNING SYSTEM OF WATER LEVELS IN FLOOD PROGRAM AREAS IN BANJARMASIN USING IoT AND LINEAR REGRESSION

Deep Learning-Based Disaster Sentiment Analysis of Unbalanced Twitter Data: The Case of Indonesia Earthquake

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