A computational approach in analyzing the empathy to online donations during COVID-19

The COVID-19 pandemic has a negative impact on many aspects of life. The global economic downturn is one of these negative consequences. Nonetheless, even though everyone feels the threat of this pandemic for themselves, some people still have the empathy to help others. An empirical analysis of this empathy attitude is expected to be a catalyst in realizing a social force for the community to work together to combat this pandemic. This study will look at how people felt about donating during the COVID-19 pandemic on Twitter. The goals of this study are to (1) compare differences in donor desire before and during the COVID-19 pandemic using the developed model, and (2) determine whether there is a significant difference in empathy for donating before and during the pandemic. This study employs computational social science (CSS) techniques to achieve this goal. The data was obtained from Twitter using the keyword "donation" in the 24 months preceding the pandemic and in the 24 months following the pandemic's arrival in Indonesia. Data analysis includes hypothesis testing using Mann-Whitney and Cohen's D statistical tests, showing a significant increase in online donation support among Indonesian Twitter users since the COVID-19 pandemic hit. From the results of data processing data obtained 159.995 data in accordance with the criteria to be analyzed. From the results of the Mann-Whitney test, all variables showed significant results between before and during the Covid-19 pandemic and in the results of the Cohen's d test, all variables got a large effect size. From the results of the two tests, it can open Twitter social media users who have increased empathy to donate during the Covid-19 pandemic in Indonesi

An Experimental Study on Sentiment Classification of Moroccan dialect texts in the web

With the rapid growth of the use of social media websites, obtaining the users' feedback automatically became a crucial task to evaluate their tendencies and behaviors online. Despite this great availability of information, and the increasing number of Arabic users only few research has managed to treat Arabic dialects. The purpose of this paper is to study the opinion and emotion expressed in real Moroccan texts precisely in the YouTube comments using some well-known and commonly used methods for sentiment analysis. In this paper, we present our work of Moroccan dialect comments classification using Machine Learning (ML) models and based on our collected and manually annotated YouTube Moroccan dialect dataset. By employing many text preprocessing and data representation techniques we aim to compare our classification results utilizing the most commonly used supervised classifiers: k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LTSM). Experiments were performed using both raw and preprocessed data to show the importance of the preprocessing. In fact, the experimental results prove that DL models have a better performance for Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.Comment: 13 pages, 5 tables, 2 figure

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining