A FrontPage Online News Analysis: An Opinion Mining Approach

Ogbuju, Emeka; Yemi-Peters, Victoria; Osumah, T.; Agbogun, J.; Ejiofor, Virginia E.

doi:10.4314/jcsia.v28i1.9

Cited by 2 publications

(2 citation statements)

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“…AI techniques used for emotion detection Ogbuju et al (2021) presented research that detects emotions from online news/text. The authors used twelve (12) different online news channels in Nigeria as datasets to train the proposed model and a hybrid method of corpus-based lexicon and dictionary approaches to achieve the research aim.…”

Section: Answers To Rq3mentioning

confidence: 99%

Techniques for facial affective computing: A review

Abdullahi,

Ogbuju,

Abiodun

et al. 2023

UESIT

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Facial affective computing has gained popularity and become a progressive research area, as it plays a key role in human-computer interaction. However, many researchers lack the right technique to carry out a reliable facial affective computing effectively. To address this issue, we presented a review of the state-of-the-art artificial intelligence techniques that are being used for facial affective computing. Three research questions were answered by studying and analysing related papers collected from some well-established scientific databases based on some exclusion and inclusion criteria. The result presented the common artificial intelligence approaches for face detection, face recognition and emotion detection. The paper finds out that the haar-cascade algorithm has outperformed all the algorithms that have been used for face detection, the Convolutional Neural Network (CNN) based algorithms have performed best in face recognition, and the neural network algorithm with multiple layers has the best performance in emotion detection. A limitation of this research is the access to some research papers, as some documents require a high subscription cost. Practice implication: The paper provides a comprehensive and unbiased analysis of existing literature, identifying knowledge gaps and future research direction and supports evidence-based decision-making. We considered articles and conference papers from well-established databases. The method presents a novel scope for facial affective computing and provides decision support for researchers when selecting plans for facial affective computing.

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Section: Answers To Rq3mentioning

confidence: 99%

Techniques for facial affective computing: A review

Abdullahi,

Ogbuju,

Abiodun

et al. 2023

UESIT

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“…To show the prevalence of the health issues caused by the reading text messages, Shalaby et al (2022) demonstrated Text2quit and Quit4baby, two texting service programs to help people cease from smoking addiction; and Text4Mood and Text4Support, two other mobile text messaging programs to help people with mood disorders and other mental health conditions, including alcohol use disorder. Health issues such as heart attack, high blood pressure, hypertension, stroke and even bowel disorder can occur due to negative messages as noted by Ogbuju et al (2021) which also demonstrated that having a system that filters and block negative emotions in text messages from getting to the user will be of great help in reducing the impact of these health matters on our communities.…”

Section: Introductionmentioning

confidence: 99%

Emotion Detection for Health and Well-being in Short Messaging Systems

Ogbuju

2023

Spek Ind

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The exposure to unpleasant emotions or content in messages can lead to health complications, including high blood pressure and several heart-related disorders. Hence, the identification of unpleasant emotions in written content can serve as a beneficial instrument in addressing certain health-related issues. Emotions can be communicated through diverse modalities, including written text, spoken language, and facial gestures. The objective of this work is to create a Text-based emotion detection system that possesses the capability to accurately identify emotions within text messages. The use of message filtering mechanisms that detect and block content containing negative emotions can serve as a preventive measure to shield users from accessing messages that have the potential to adversely impact their well-being. Conversely, messages that convey positive or neutral emotions remain accessible for comprehension. In order to accomplish this objective, a combination of three machine learning algorithms, namely Naive Bayes, Support Vector Machine, and Logistic Regression, were employed, adhering to the CRISP-DM approach. The Logistic Regression technique achieved the greatest accuracy rate of 98.4% and was employed in the construction of the detection system. The Graphical User Interface (GUI) of the system was developed utilizing HTML and CSS, with the integration of diverse components to establish a comprehensive and operational interface for the user.

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A FrontPage Online News Analysis: An Opinion Mining Approach

Cited by 2 publications

References 0 publications

Techniques for facial affective computing: A review

Techniques for facial affective computing: A review

Emotion Detection for Health and Well-being in Short Messaging Systems

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