Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

Abstract

All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.

Parking detection system using background subtraction and HSV color segmentation

“…The accuracy test with the confusion matrix has four terms to represent the results of the classification process, namely true positive (TP), true negative (TN), false positive (FP), and false negative (FN) [27]. The TP value is obtained if the number of parking slots filled with vehicles is detected correctly by the system, and the TN value is obtained if the number of available (empty) parking slots is detected correctly by the system.…”

Detecting Presence of PTSD Using Sentiment Analysis From Text Data

“…Next is GB, which has been used in sentiment analysis for social media text, particularly related to mental health (57)(58)(59). Gradient Boosting is a sequential boosting method that large trees that concentrate on misclassified observations, found by using the gradients of large residuals computed in previous iterations to refine future predictions (60).…”

“…A recent paper examined detecting anxiety based on social media data related to the COVID-19 pandemic. Their study used sentiment analysis with a number of different models, including Extreme Gradient Boosting (XGB) (59), which is a stochastic version of GB, and is computationally faster for large datasets. Their XGB model achieved an accuracy of 73.2 %, but had the highest recall with 0.87 against other models such as K-nearest neighbors, SVC, RF, and decision trees (59).…”

“…Their study used sentiment analysis with a number of different models, including Extreme Gradient Boosting (XGB) (59), which is a stochastic version of GB, and is computationally faster for large datasets. Their XGB model achieved an accuracy of 73.2 %, but had the highest recall with 0.87 against other models such as K-nearest neighbors, SVC, RF, and decision trees (59). Another study looked at detecting anxiety and depression from social media data using word frequencies, timing, and sentiments (58).…”

Text Mining Literature Review on Indonesian Social Media

“…Adapun judul literatur yang diteliti dilambangkan dengan huruf "R" dan tahun terbit literatur direpresentasikan dengan warna chart berupa jingga (Naive Bayes), hijau (K-NN), dan kuning (SVM). Kemudian Gambar 6 menunjukkan literatur Naive K-NN yang diteliti adalah literatur terbitan tahun 2015-2021 [14], [16], [22], [24]- [28]. Berikutnya, Gambar 7 menunjukkan literatur SVM yang diteliti adalah literatur terbitan tahun 2015-2020 [16], [21]- [23], [27], [29]- [34].…”