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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
P. Tamil Selvan
Karpagam Academy of Higher Education
Electrical & Electronics Engineering

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Reduction of false negatives in multi-class sentiment analysis Chris Aloysius; P. Tamil Selvan
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4682

Abstract

Sentiment analysis classifications are done as positive, negative, as well as neutral ones. The increased usage of social media and its effects on society call for a more thorough, fine-grained explanation than that. In this study, classification is done in five classes-strongly positive, weakly positive, neutral, weakly negative, and strongly negative-in a more precise manner. Instead of using the typical ways of measuring accuracy alone, a novel method to eliminate false negatives (FN) is focused together with a fine-grained categorization. A bigger risk in sentiment analysis is a false negative. FN classification occurs when the context's polarity is identified as True when it is actually false. A complex dataset is used in this research for the experimental study, and the entire dataset is separated into five classes. Each class's FN are assessed using the suggested methodology. Comparing the proposed strategy to other, it was found to achieve about 53% more reduction in FN cases than rule based models and better predictions than compared machine learning models.
Leveraging CNN to analyze facial expressions for academic engagement monitoring with insights from the multi-source academic affective engagement dataset Noora C. T.; P. Tamil Selvan
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp977-999

Abstract

The dynamics of student engagement and emotional states significantly influence learning outcomes. Positive emotions, stemming from successful task completion, contrast with negative emotions arising from learning struggles or failures. Effective transitions to engagement occur upon problem resolution, while unresolved issues lead to frustration and subsequent boredom. Facial engagement monitoring is crucial for assessing students’ attention, interest, and emotional responses during learning. Recent advancements in convolutional neural networks (CNNs) show promise in automatically analyzing facial expressions to infer engagement levels. This study proposes a CNN-based approach utilizing the multi-source academic affective engagement dataset (MAAED) to categorize facial expressions into boredom, confusion, frustration, and yawning. By extracting features from facial images, this method offers an efficient and objective means to gauge student engagement. Recognizing and addressing negative affective states, such as confusion and boredom, is fundamental in creating supportive learning environments. Through automated frame extraction and model comparison, this study demonstrates reduced loss values with improving accuracy, showcasing the effectiveness of this method in objectively evaluating student engagement. Facial engagement monitoring with CNNs, using the MAAED dataset, is pivotal in understanding human behavior and enhancing educational experiences. The CNN model, trained on MAAED annotated facial expressions, accurately classifies engagement categories. Experimental results underscore the CNN-based approach’s efficacy in monitoring facial engagement, highlighting its potential to enrich educational environments and personalized learning experiences.
Co-Authors Chris Aloysius Noora C. T.