Twitter Sentiment Analysis on Activities of Saudi General Entertainment Authority

Alkhaldi, Sara; Alzuabi, Sultana; Alqahtani, Ryoof; Alshammari, Amjad; Alyousif, Fatimah; Alboaneen, Dabiah; Almelihi, Modhe

doi:10.1109/iccais48893.2020.9096738

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…For the same goal as [18] but using a different methodology, Alkhaldi et al [19] aimed to analyze individuals' opinions on Twitter regarding the activities of the Saudi GEA using machine and deep learning techniques. The authors collected 3817 tweets using RapidMiner.…”

Section: Saudi Emotions During the Covid-19 Pandemicmentioning

confidence: 99%

“…In March 2020, the World Health Organization (WHO) [1] declared the outbreak of the novel Coronavirus disease (COVID- 19), first identified in Wuhan, China, in December 2019, as a global pandemic. Since then, COVID-19 has precipitated a global crisis, with more than 214,468,601 confirmed cases and more than 4,470,969 deaths worldwide as of 27 August 2021 [1].…”

Section: Introductionmentioning

confidence: 99%

“…During the Eid Al-Fitr holiday, the authorities imposed a country-wide 24-h curfew that started on 23 May 2020 and lasted for five days. Moreover, school closures were enforced between 9 March 2020 and 29 August 2021, mosque closures were enforced between 15 March 2020 and 31 May 2020, and national flight lockdowns were enforced between 19 March 2020 and 17 May 2021. Research has found an increase in gaming duration and overall online activity during these lockdown periods [6].…”

Section: Introductionmentioning

confidence: 99%

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Understanding Entertainment Trends during COVID-19 in Saudi Arabia

2022

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Studying people’s opinions is a growing research field. This area of research is known as sentiment analysis. The COVID-19 pandemic changed everything around the world and reduced social contact among people. Citizens and residents of Saudi Arabia experienced high stress during the pandemic, seeking entertainment via games and publishing their activities on social media platforms such as Twitter. In this paper, we focus on applying the Mazajak sentiment analyzer on tweets containing game keywords in Arabic collected using Twitter API during the lockdown period to decide whether users preferred playing individually or in groups. This can help designers and developers, as well as the Saudi General Entertainment Authority (GEA), focus on creating the most interesting games for individuals and improving them to meet users’ expectations. Our approach has three main stages: tweet collection, tweet preparation, and finally, the application of sentiment analysis to get the desired goal based on people’s behavior toward the games. The result of this paper confirms that people, in general, preferred playing in groups, instead of alone, during this period.

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Section: Saudi Emotions During the Covid-19 Pandemicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding Entertainment Trends during COVID-19 in Saudi Arabia

2022

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“…2.4 Text ProcessingText Preprocessing pada penelitian ini, menerapkan serangkaian tahap secara berurutan yaitu case folding, cleaning, tokenizing, stopword removal, stemming[23].…”

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Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia

Arsi

Wahyudi

Waluyo

2021

RESTI

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President Joko Widodo decided to move the capital city of the country outside Java. The relocation of the capital city is contained in the 2020-2024 National Medium-Term Development Plan. Community response to this has been mixed through national television and social media, especially Twitter. The tendency of Twitter users to respond to the government discourse can be seen with sentiment analysis. Sentiment analysis is one of the areas of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions. In this study, the Feature Selection PSO algorithm in the classification of the SVM model is proposed to improve the resulting accuracy in the sentiment analysis of moving capital cities. Experiments on the data of 1,319 tweets (457 positive sentiments and 862 negative sentiments) indicate an increase in accuracy by 2.09% from 79.06% to 81.15%, with the classification category is “Good Classification”.

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A Fuzzy Model for Analyzing Students’ Feedback About e-Learning

Alzaid,

Fkih

2023

Lecture Notes in Networks and Systems

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Twitter Sentiment Analysis on Activities of Saudi General Entertainment Authority

Cited by 7 publications

References 5 publications

Understanding Entertainment Trends during COVID-19 in Saudi Arabia

Understanding Entertainment Trends during COVID-19 in Saudi Arabia

Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia

A Fuzzy Model for Analyzing Students’ Feedback About e-Learning

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