Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets

Abstract

This paper proposes a system to analyze the sentiments of tweeters. It is to build an accurate model to detect different emotions in a tweet. The analysis takes place through several stages (i.e., pre-processing, feature extraction, and training more than one machine learning (ML)). Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes were selected as supervised machine learning for sentiment analysis using a dataset of 3,057 tweets with users ranging from fear to happiness, anger, and sadness because this method is suitable for solving a problem of this type. This system was also applied to another dataset of 10,000 Tweets (5,000 positive and 5,000 negatives). This approach, consisting of three Naïve Bayes classification models, was applied to two datasets to analyze the sentiment used in them and classify each category separately. The Multinomial Naïve Bayes model outperformed the other models Where it achieved an accuracy of (91.6%) when applied to the first dataset and accuracy (87.6%) when applied to the second dataset. The researchers aim to continue this research with larger data by using other methods of sentiment analysis to predict users' thoughts about COVID-19 or any other problem and to obtain higher accuracy for the models used.

On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19

“…However, among the machine learning models, the Multinomial Naïve Bayes outperformed the other models, consistent with other studies. For example, a Naïve Bayes classifier has been used for authorship classification of tweets 70 , for identification of disaster-related informative tweets 71 , 72 , identifying tweets with hate content 73 , classifying tweets into topic-based categories 74 , and for sentiment analysis of COVID-19 tweets 75 . In our work, the Naïve Bayes classifier resulted in 61% training accuracy and 65% test accuracy, with a 65% macro-average -score.…”

Analisis Klasterisasi Untuk Prediksi Jumlah Kasus DBD Berdasarkan Jenis Kelamin Dan Kabupaten/Kota Di Jawa Barat

“…Naïve bayes merupakan metode klasifikasi dalam bidang ilmu data dan statistik yang mengandalkan prinsip probabilitas. Meskipun dinamakan "naif" karena mengasumsikan independensi antara fitur-fitur data, teknik ini umumnya digunakan untuk meramalkan kategori entitas dengan mempertimbangkan probabilitas keterkaitan fitur-fitur tersebut [12].…”

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

“…Globally, there are approximately 4 billion internet users and applications; in the Middle East, the number of users has grown from 147 million to 164 million during the previous few years [2]. With the increasing number of social media users sharing their opinions or leaving reviews or feedback about particular services or products [3], it is no secret to anyone today the role played by reviews, opinions or feedback on various things, whether they are comments on social media or user-written reviews about a particular service or product [4,5]. Since the Arabic language is an official language in 22 countries around the world [6][7][8], it is also the 4th most used language on the Internet.…”