Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782123
Title: Automatic emotion recognition in English and Arabic text
Author: Al-Mahdawi, Amer
ISNI:       0000 0004 7967 7291
Awarding Body: Bangor University
Current Institution: Bangor University
Date of Award: 2019
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Abstract:
This study investigated the automatic recognition of emotion in English and Arabic text. We perform experiments with a new method of classification for recognising emotions using the Prediction by Partial Matching (PPM) character-based text compression scheme. These experiments involve both document level classification (whether a text of document is emotional or not) and also fine-grained classification such as recognising Ekman's six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three English datasets (the LiveJournal's blogs dataset, Aman's blogs dataset, and Alm's fairy tales dataset) show that the new method signicantly outperforms the traditional word-based text classification methods. The results show that the PPM compression-based classification method is able to distinguish between emotional and non-emotional text with high accuracy, between texts involving Happiness and Sadness emotions (with 79.1% accuracy for Aman's dataset and 76.9% for Alm's datasets) and texts involving Ekman's six basic emotions for the LiveJournal dataset (87.4% accuracy). Results also show that the method outperforms traditional feature-based classifiers such as Naive Bayes and SMO in most cases in terms of accuracy, precision, recall and F-measure. In order to see how well the classifier performs on another language not related to English and also in order to create another Arabic benchmark corpus for future emotion classification experiments, we created a new Iraqi Arabic Emotion Corpus (IAEC) dataset annotated according to Ekman's basic emotions. This dataset is composed of Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. We then explored six different supervised machine learning methods to test the new dataset. We used standard Weka classifiers ZeroR, J48, Naive Bayes, Multinomial Naive Bayes for Text and SMO. We compared these results with our compression-based classifier PPM. Our study reveals that the PPM classifier significantly outperforms the other classifiers for the new dataset achieving the highest results in terms of accuracy, precision, recall, and Fmeasure. We also designed and investigated another new classification technique motivated by information divergence to recognize Ekman's emotions in text. We used the three datasets written in the English Language and the one in the Arabic Language to evaluate the new method. The new method was able to achieve a better result for Alm's dataset in terms of accuracy, precision, recall and F-measure than PPM and standard Weka classifiers. The new method also outperforms all standard Weka classifiers for all four datasets. Finally, these results show that our proposed technique is promising as an alternative technique for English and Arabic text categorization in general.
Supervisor: Teahan, William Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.782123  DOI: Not available
Keywords: PPM ; text categorisation ; text classification ; emotion recognition ; classification
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