Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic

Soomro, Zainab Tariq; Ilyas, Sardar Haider Waseem; Yaqub, Ussama

doi:10.1109/besc51023.2020.9348291

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…In this study author compared the model in binary and multiclass data to find this model is to find out if this model is suitable for application to binary or multi-class datasets. The MNB model alpha parameters starting (0, 1, 0.01, 0.001), gridCV with cv = 3, and n-gram parameters, namely (1, 1), (1, 2), (1,3), (1,4) by testing the two proposed models (added negation) and compared to the dataset without adding negation. In Table 1 and Table 2 it can be seen that the classification of the proposed model gets the best parameter results on the MNB model alpha 0.01 parameter and feature extraction from the combination (1, 4) unigram with ngram.…”

Section: Classification Using Mnbmentioning

confidence: 99%

“…The classification was carried out twice experiment to compare the results of the classification of two classes and three classes, namely the first classification of positive and negative classes, then compared with positive, negative, and neutral classes. Classification using the multinomial nave Bayes (MNB) algorithm using MNB model alpha parameters starting (0, 1, 0.01, 0.001), gridCV with cv = 3, and n-gram parameters, namely (1, 1), (1,2) , (1,3), (1,4) by testing the two proposed models (added negation) and compared to the dataset without adding negation [19]. Multinomial Naive Bayes (MNB) is an algorithm that can classify a set of texts and documents.…”

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confidence: 99%

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Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

Ramadhan

Purwanto

Alzami

2022

KINETIK

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The use of the internet globally, especially on the use of social media, includes Indonesia as one of the most active users in the world. The amount of information that can be obtained can be used to be processed into useful information, for example, information about the public sentiment on a particular topic. Tracking and analyzing tweets can be a method to find out people's thoughts, behavior, and reactions regarding the impact of Covid-19. The key to sentiment analysis is the determination of polarity, which determines whether the sentiment is positive or negative. The word negation in a sentence can change the polarity of the sentence so that if it is not handled properly it will affect the performance of the sentiment classification. In this study, the implementation of negation handling on sentiment analysis of Indonesian people's opinions regarding COVID-19 on Twitter has proven to be good enough to improve the performance of the classifier. Accuracy results obtained are 59.6% compared to adding negation handling accuracy obtained is 59.1%. Although the percentage result is not high, documents that include negative sentences have more meaning than negative sentences. However, for the evaluation using the MCC evaluation matrix, the results were quite good for the testing data. For the results of the proposed method whether it is suitable for data that has two classes or three classes when viewed from the results of the evaluation matrix, the proposed method is more suitable for binary data or data that has only two classes.

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Section: Classification Using Mnbmentioning

confidence: 99%

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confidence: 99%

Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

Ramadhan

Purwanto

Alzami

2022

KINETIK

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“…Maka dari itu, diperlukan sebuah peneltian yang mengangkat permasalahan ini. Saat ini, masyarakat menjadikan media sosial sebagai wadah bagi mereka untuk mengekspresikan pendapat terkait pandemi COVID-19, salah satunya adalah Twitter [3]. Twitter merupakan media sosial yang cukup lengkap, karena pada Twitter terdapat opini yang variatif dan sesuai dengan bidangnya masing-masing, seperti berita harian, politik, pendidikan, dan bahkan bisnis.…”

Section: Pendahuluanunclassified

Analisis Sentimen terhadap Peluang Kerja di Indonesia selama Masa Pandemi COVID-19 dengan Metode Klasifikasi Naive Bayes

Abdullah

Mahdiana

2022

mib

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The impact of the COVID-19 pandemic is very broad, one of which is in the business sector. This has resulted in an impact on job opportunities during the COVID-19 pandemic in Indonesia. This study aims to conduct in-depth learning related to job opportunities in Indonesia during the COVID-19 pandemic using the Naive Bayes model. The data source used comes from Twitter. The results of this study indicate that the largest AUC score falls to the Random Forest model (79.40%), but for more accurate precision falls to the Naive Bayes model (87.88%). In addition, there is a confusion matrix which shows that the Naive Bayes model trial is running well.

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“…With respect to the COVID-19 pandemic, many studies were conducted on the Twitter Social Network: [1] focused on Twitter Arabic and designed a graph structure where nodes are the users and arches with predefined weights represent the relationships between users; [18] focused on the #CoronavirusPandemic trend and measured the influence of Twitter users by combining the attributes of the users' profile with the underlying network type formed among users. [24] focused on a possible correlation between the number of Twitter mentions and the number of new COVID-19 cases. [12] proposed a novel metric to measure the influence a user might have on specific conversations and, therefore, to identify opinion leaders within Twitter conversations.…”

Section: Related Workmentioning

confidence: 99%

On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

Furini

Mariotti

Montangero

2022

Proceedings of the 2022 ACM Conference on Information Technology for Social Good

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What happened on social media during the recent pandemic? Who was the opinion leader of the conversations? Who influenced whom? Were they medical doctors, ordinary people, scientific experts? Did health institutions play an important role in informing and updating citizens? Identifying opinion leaders within social platforms is of particular importance and, in this paper, we introduce the idea of a time sensitive interaction graph to identify opinion leaders within Twitter conversations. To evaluate our proposal, we focused on all the tweets posted on Twitter in the period 2020-21 and we considered just the ones that were Italian-written and were related to COVID-19. After mapping these tweets into the graph, we applied the PageRank algorithm to extract the opinion leaders of these conversations. Results show that our approach is effective in identifying opinion leaders and therefore it might be used to monitor the role that specific accounts (i.e., health authorities, politicians, city administrators) have within specific conversations.

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Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic

Cited by 7 publications

References 10 publications

Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

Analisis Sentimen terhadap Peluang Kerja di Indonesia selama Masa Pandemi COVID-19 dengan Metode Klasifikasi Naive Bayes

On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

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