No abstract

The classification of users' sentiment from social media data can be used to learn public opinion on certain issues. The presence of sarcasm in sentences can hamper the performance of the classification as it tends to "fool" the system. In this paper, we investigate mechanisms for detecting sarcasm in Malay social media data that contain sarcastic contents; more specifically the public comments on economic related posts on Facebook. Two features were investigated; the n-gram and punctuation marks. Features selection in the form of Pearson's correlation was then applied to reduce the features size. To measure the performances of the selected features, two supervised classification techniques were employed which are k-Nearest Neighbors and non-linear Support Vector Machine. Experiments on sarcasm detection and classification were conducted. Results show that combination of n-gram and punctuation marks produced the best F-measure and Area Under Curve of 0.818 for sarcasm detection. Extended experiment on sarcasm classification recorded F-measure of 0.991 with Area Under Curve of 0.994 for sarcasm positivity while F-measure of 0.902 with Area Under Curve of 0.846 for sarcasm negativity.

<span>Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.</span>

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.