emotions detection of arabic text
201622752
Recent Advances of Affect Detection from Arabic Text
Emotion Detection (ED) from text has been an active research field recently. It has attracted the attention of researchers as it can measure the emotional contexts while humans interact with computers. Humans could express their emotion in various ways; using typed text, facial expressions, speech, gestures, and physiological measures. ED is considerably different from sentiment analysis SA, where SA goal is to detect polarity from text such as positive, negative or neutral. On the other hand, ED aims to recognize emotions from input text. Emotions can be modeled as discrete categories, e.g. Ekmans six basic emotions (angry, fear, joy, disgust, surprise and sadness) On the other hand there is the dimensional model that express emotions as valence, arousal and dominance values. Social media provides a rich source of emotional text, e.g. Twitter and Facebook. In this paper, we provide a review of recent work on ED from Arabic text. We discuss approaches (lexicons, machine learning, deep neural networks and ensemble approaches) tools for text processing, and We also discuss description of the most popular datasets in this domain.
2019 10th International Conference on Information and Communication Systems (ICICS)
Saja Khaled Tawalbehe Omar Alzoubi Mohammad Al-Smadi
58675413
SEDAT: Sentiment and Emotion Detection in Arabic Text Using CNN LSTM Deep Learning
Social media is growing as a communication medium where people can express online their feelings and opinions on a variety of topics in ways they rarely do in person. Detecting sentiments and emotions in text have gained considerable amount of attention in the last few years. The significant role of the Arab region in international politics and in the global economy have led to the investigation of sentiments and emotions in Arabic. This paper describes our system SEDAT, to detect sentiments and emotions in Arabic tweets. We use word and document embeddings and a set of semantic features and apply CNN LSTM and a fully connected neural network architectures to obtain performance results that show substantial improvements in Spearman correlation scores over the baseline models.
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
Malak Abdullah Mirsad Hadzikadic Samira Shaikh
14291749
A Computational Approach for Analyzing and Detecting Emotions in Arabic Text
The field of Affective Computing (AC) expects to narrow the communicative gap between the highly emotional human and the emotionally challenged computer by developing computational systems that recognize and respond to the affective states of the user. Affect sensitive interfaces are being developed in number of domains, including gaming, mental health, and learning technologies. Emotions are part of human life. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper concerned with the automatic detection of emotions in Arabic text. This construction is based on a moderate sized Arabic emotion lexicon used to annotate Arabic children stories for the six basic emotions: Joy, Fear, Sadness, Anger, Disgust, and Surprise. Our approach achieves 65% accuracy for emotion detection in Arabic text.
Amira F El Gohary Torky Sultan Maha Attia Hana Mohamed M El Dosoky Mohamed M El
221858880
Ontology based Emotion Detection in Arabic Social Media
With the spread of social media and its content that is often driven by user's emotions or their opinions on some topic, efforts have been made to provide effectiveness mechanisms for automatic emotion detection, to employ it in various fields. With the spread of Arabic language on those sites, however Arabic studies in emotion detection are still shy somewhat, perhaps, due to the difficult dealing with Arabic language especially with slang that is used mainly in social media, in the absence of unified representation of emotion in Arabic, this was motivation to build an ArEmontology ontology, for conceptual representation of emotions in Arabic with standard language based on emotional theories, in addition to propose an effective mechanism to detect emotion from Arabic text by using the classification and semantic relations in ArEmontology, this mechanism was applied on Facebook 's posts, as it is the most site used by Syrian, and got reasonable accuracy of 65% in detecting one of the emotional categories: joy, sadness, fear, anger, surprised and disgust.
Sawsan N Cassab Mohamad-Bassam Kurdy
222539600
Cyber Bullying and Cyber Harassment Detection Using Supervised Machine Learning Techniques in Arabic Social Media Contents
The social media has provided users with the chance to publish their written and multimedia content and express feelings and emotions about particular subjects via the internet. However, some users have abused these platforms by performing various acts such as Cyber Bullying and Cyber Harassment. These phenomena are dangerous and have negative psychological, health, and social effects. Although multiple works have focused on detecting these phenomena on English text, few works studied this phenomenon on Arabic. Moreover, these works used limited number of methods and datasets. Furthermore, there is a lack in Arabic datasets that are concerned with this topic. We propose the use of Machine Learning to detect such negative written acts. We apply various classification algorithms to the dataset, and we use various Arabic Natural Language Processing (NLP) tools. To evaluate the performance of the classifiers, we use Recall, Precision, and F1 Measure. The results show that the Random Forest algorithm yields the highest values of F1 Measure. The same results occurred when no stemming and no stop word removal are applied. However, when separating datasets into Facebook Posts dataset and Twitter Tweets dataset, SVM gives the highest F1 Measure value. Significant tests were conducted to support our results.
Tarek Kanan Amal Aldaaja Bilal Hawashin
225130187
Affect detection from arabic tweets using ensemble and deep learning techniques
Abstract Affect detection from text has captured the attention of researchers recently. This is due to the rapid use of social media sites (e.g. Twitter, Facebook) which allows users to express their feelings, emotions, and thoughts in textual format. Analyzing emotion rich textual data of social networks has many real life applications. The context of an emotional text can be measured by analyzing certain features of this rich source of emotional information. Classifying text into emotional labels/intensities is considered a difficult problem. This paper resolves one of the state of the art NLP research emotion and intensity detection tasks using Deep Learning and ensemble implementations. In this paper, we developed several innovative approaches; (a) bidirectional GRU_CNN (BiGRU_CNN) (b) conventional neural networks (CNN) and (c) XGBoost regressor (XGB) The ensemble of BiGRU_CNN, CNN, and XGB is used to solve an emotion intensity (EI reg) task of the SemEval 2018 Task1 (Affect in Tweets) Our proposed ensemble approach was evaluated using a reference dataset of the SemEval 2018 Task1. Results show that our approach is well above the baseline for this task. It also achieved a Pearson of (69.2% with an enhancement of 0.7% in comparison with previous best performing models.
Omar Alzoubi Saja Khaled Tawalbeh Mohammad Al-Smadi
52967196
Emotional Tone Detection in Arabic Tweets
Emotion detection in Arabic text is an emerging research area, but the efforts in this new field have been hindered by the very limited availability of Arabic datasets annotated with emotions. In this paper, we review work that has been carried out in the area of emotion analysis in Arabic text. We then present an Arabic tweet dataset that we have built to serve this task. The efforts and methodologies followed to collect, clean, and annotate our dataset are described and preliminary experiments carried out on this dataset for emotion detection are presented. The results of these experiments are provided as a benchmark for future studies and comparisons with other emotion detection models. The best results over a set of eight emotions were obtained using a complement Naive Bayes algorithm with an overall accuracy of 68.12%
CICLing
Amr Al-Khatib Samhaa R El-Beltagy
61209191
Towards Expressive Arabic Text to Speech
x Chapter1: Introduction 1 1.1 Expressive TTS literature review 1 1.1.1 Acoustic correlates of emotions 1 1.1.2 Generating expressive speech 1 1.2 Motivation and contribution of the thesis 4 1.3 Thesis Organization 4 Chapter2: Overview of Text To Speech systems 5 2.1 Text and phonetic Analysis 5 2.1.1 LEXICON 7 2.1.2 DOCUMENT STRUCTURE DETECTION 7 2.1.3 TEXT NORMALIZATION 7 2.1.4 LINGUISTIC ANALYSIS 8 2.1.5 HOMOGRAPH DISAMBIGUATION 9 2.1.6 MORPHOLOGICAL ANALYSIS 9 2.1.7 LETTER TO SOUND CONVERSION 9 2.2 Prosody Generation 9 2.3 Acoustic Synthesis 10 2.3.1 First generation acoustic synthesizers 10 2.3.2 Second generation acoustic synthesizers (diphone concatenative synthesizers) 11
Doaa Gamal Madany Taya
59906875
Feel the Heat: Emotion Detection in Arabic Social Media Content
The automatic detection of emotions in textual parts of social media websites such as Facebook and Twitter has applications for business development, user interface design, content creation, emergency response, among others. Current research has shown that it is possible to detect emotions for English content. To our knowledge, however, there are only few attempts for Arabic content. There is neither Arabic corpus with instances labeled for emotions, nor studies to detect emotions from Arabic microblogs content. Therefore, we collected Arabic text messages from the social networking website Twitter from January/February 2011. Human annotators labeled them with the corresponding emotions. Working with that corpus, our experiments show that emotions can be automatically detected from tweets after performing Arabic language related language preprocessing steps. Our contribution consists in adding preprocessing steps that have improved the classification results by 4.4% compared to the original Khoja stemmer. In addition, we have extracted a sample word emotion lexicon from that corpus. Our experiment demonstrates that this sample word emotion lexicon enhances the emotion detection results by 22.27% compared to the SMO classification using the train/test option. Finally, we show that the communication style used by the writer significantly relates with the emotion expressed in the text.
ICDM 2014
Omneya Rabie Christian Sturm
219602401
COMPARISON STUDY OF AUTOMATIC CLASSIFIERS PERFORMANCE IN EMOTION RECOGNITION OF ARABIC SOCIAL MEDIA USERS
Emotion recognition from text gained a lot of interest in the last years, but some languages such as Arabic (with its different spoken dialects) have not been given such attention. In this paper, we present our work in the Emotion detection of Arabic texts, with a focus on Levantine Twitter Messages. We have constructed a corpus of Arabic Levantine tweets, and annotated it with correspondent emotions. We implemented different methods to automatically classify text messages of individuals to infer their emotional states. We compared the results of different machine learning algorithms, and the inclusion of different features, to determine the best configuration of the emotion recognition system.
Abdullah Daood Issam Salman Nada Ghneim