Mood Detection Based on Arabic Text Documents using Machine Learning Methods

Hussein, Abdelbaset

doi:10.30534/ijatcse/2020/36942020

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Furthermore, a machine-learning method based on Arabic text was presented by Hussein et al 14 for the detection of emotion in text-mining data. They collected text-mining data from various blogs on the internet and social media platforms.…”

Section: /17mentioning

confidence: 99%

WITHDRAWN: Emotion Detection in Arabic Short Text: A Deep-Learning Approach

Aljwari*,

Zamzami

2023

Preprint

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In modern society, writing down thoughts, ideas, and pleasant experiences has become a widespread way to express feelings. X, formerly known as Twitter, is a rich data source that companies can use to study individuals’ ideas, thoughts, and emotions for a range of useful applications. The analysis of positive and negative feelings is a key focus of NLP research literature, while emotion detection receives relatively little attention. Very few studies to date have examined the classification of emotions in text, particularly Arabic written content. The new study uses deep learning approaches to solve this difficulty and close these existing gaps in the literature. A number of different deep learning models are available and each one has been developed based on a unique feature engineering approach to classify the emotions conveyed in the SemEval-2018 dataset into four groups, namely joy, fear, anger and sadness. The results show that the CNN model that employs Word2vec outperforms the other models, with an accuracy of 80%. Furthermore, it has been found to perform better in Arabic than the most current comparable model, with improvements varying between 12–54.96% for F1 scores to 5–26% for accuracy.

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Section: /17mentioning

confidence: 99%

WITHDRAWN: Emotion Detection in Arabic Short Text: A Deep-Learning Approach

Aljwari*,

Zamzami

2023

Preprint

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“…Hussein et al. ( 2020 ) proposes a framework for mood detection and prediction for Arabic language. A framework for detection of mood from song lyrics in Bangla has been proposed in Urmi et al.…”

Section: Literature Surveymentioning

confidence: 99%

Mood detection and prediction using conventional machine learning techniques on COVID19 data

Bhattacharya

Agarwala

Roy

2022

Soc. Netw. Anal. Min.

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Emotion detection is a promising field of research in multiple perspectives such as psychology, marketing, network analysis and so on. Multiple models have been suggested over the years for accurate and efficient mood detection. Identifying emotion, or mood, from text has progressed from a simple frequency distribution analysis to far more complicated learning approaches. The main aim of all these text mining and analysis is twofold. First is to categorise existing text into broad classes of emotions, such as happy, sad, angry, surprised and so on. The second aim is to accurately predict the moods of real-time streaming text. The novelty of the work lies in the extensive comparison of nine conventional learning methods with respect to performance metrics precision, recall, F1 and accuracy as well as studying the variance of mood over time using a wide array of moods (25). Using conventional classifiers allow near real-time predictions, can work on considerably less training data, and has the flexibility of feature engineering, as deep learning methods have feature engineering embedded in the model. Since a single line of text can be associated with multiple emotions, this article compares the performance of classifiers in predicting multiple moods for streaming text with likelihood-based ranking. An android application named Citizens’ Sense was developed for text collection and analysis. The performance of mood classifiers are tested further using Twitter data related to COVID19. Based on the precision, recall, F1 and accuracy of the classifiers, it can be seen that Random Forest, Decision Tree and Complement Naive Bayes classifiers are marginally better than the other classifiers. The variance of mood over time, and predicted moods for text support this finding.

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“…Many researchers have applied neural network to classify human facial expressions, in researches [5,7] the concepts of committee neural network using which includes several sets of neural network algorithms are applied in the training face for the aim of image classification. It is found in the mentioned researches that committee neural networks outperform a single neural network in detecting and classification each part of the input tested images.…”

Section: Image Processingmentioning

confidence: 99%

Mood Detection using Facial Recognition Technology Application in Higher Education Institution

2020

IJATCSE

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Mood detection is an important trait that human can do using face expressions and it could be learned by the machine using machine learning approaches and technology. This research utilized a deep learning algorithm to develop a model that can detect various mood of participants in order to identify their subject areas of interest when enrolling in higher education institution so the academic advisor can smartly advise and guide the students in selecting the right program when they are seeking admission to the college.The methodology used in this research is application of the pre trained model technology deployed by convolution neural network for analyzing the facial expression on participants through three steps namely: image processing, facial recognition and mood detection. The mood detection by facial recognition technology is used to test independent input images of 15 participants to detect whether the mood is "happy", "sad", "neutral" and "surprise". The student's mood is determined by the facial movement this can further help in guiding the students for the proper program selection based on their facial expressions. A quasi experimental approach has been taken to analyze the results. The research findings show that the developed model was able to predict and classify various mood quiet accurately such as "happiness" and "neutral". On the other hand, difficultly detect other moods such as "surprise" and "sad".The study shows promising results and great capability of detecting human moods that has lots of applications such as academic advising in academic institution. The research is yet a good contribution in identifying a student's facial expression that positively effects the academic advising process and it is expected to better understand the student's needs and emotions.

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Mood Detection Based on Arabic Text Documents using Machine Learning Methods

Cited by 5 publications

References 29 publications

WITHDRAWN: Emotion Detection in Arabic Short Text: A Deep-Learning Approach

WITHDRAWN: Emotion Detection in Arabic Short Text: A Deep-Learning Approach

Mood detection and prediction using conventional machine learning techniques on COVID19 data

Mood Detection using Facial Recognition Technology Application in Higher Education Institution

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