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Detecting Emotions in English and Arabic Tweets

Tariq Ahmad, Allan Ramsay, Hanady Ahmed
2019 Information  
We show that this approach outperforms the use of DNNs and other standard algorithms.  ...  We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  ... 
doi:10.3390/info10030098 fatcat:aa2hi3fmpnarrixqkvrza7zbba

TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning

Malak Abdullah, Samira Shaikh
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
This paper describes TeamUNCC's system to detect emotions in English and Arabic tweets.  ...  Task 1 in the International Workshop SemEval 2018, Affect in Tweets, introduces five subtasks (El-reg, El-oc, V-reg, V-oc, and E-c) to detect the intensity of emotions in English, Arabic, and Spanish tweets  ...  The models built for detecting emotions related to Arabic tweets ranked third in subtask El-oc and fourth in the other subtasks.  ... 
doi:10.18653/v1/s18-1053 dblp:conf/semeval/AbdullahS18 fatcat:5u4d67mdjvh2hmbh7onpuwk4j4

Emotion based voted classifier for Arabic irony tweet identification

Nikita Kanwar, Rajesh Kumar Mundotiya, Megha Agarwal, Chandradeep Singh
2019 Forum for Information Retrieval Evaluation  
In this paper, we have worked on irony detection in the Arabic language, a task which is organized by FIRE 2019.  ...  The tweets have been preprocessed and tokenized to extract the frequency-based, emotion-based features. These features are used to irony identification using the voted classifier.  ...  Irony detection in Arabic tweets is a challenging task due to the inclusion of dialects, non-diacritised texts, data sparsity, and code-switching with Arabic dialects, French and English [12] .  ... 
dblp:conf/fire/KanwarMAS19 fatcat:midmh33zs5fv5npqyohdf2zhrm

Cross-lingual Emotion Detection [article]

Sabit Hassan, Shaden Shaar, Kareem Darwish
2022 arXiv   pre-print
In our study, we consider English as the source language with Arabic and Spanish as target languages.  ...  Thus, we explore the efficacy of cross-lingual approaches that would use data from a source language to build models for emotion detection in a target language.  ...  We focused on using English emotion detection training data to train models that can tag Arabic/Spanish tweets using 11 different emotions with minimal drop in effectiveness compared to monolingual models  ... 
arXiv:2106.06017v2 fatcat:o2tu2gn6rrfilosje247cggszq

Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification

Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babaoglu
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets.  ...  In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 "Affect in Tweets", subtask E-c "Detecting Emotions (multi-label classification)".  ...  such as stem+stop in Arabic and lem+stop in both English and Spanish.  ... 
doi:10.18653/v1/s18-1024 dblp:conf/semeval/MulkiAHB18 fatcat:yajfglv6mnctrfj45szmwemroa

EMA at SemEval-2018 Task 1: Emotion Mining for Arabic

Gilbert Badaro, Obeida El Jundi, Alaa Khaddaj, Alaa Maarouf, Raslan Kain, Hazem Hajj, Wassim El-Hajj
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks.  ...  While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic.  ...  English Emotion Analysis In general, there are three different approaches for emotion classification: keyword-based detection, learning-based detection, and hybrid detection (Avetisyan et al., 2016) .  ... 
doi:10.18653/v1/s18-1036 dblp:conf/semeval/BadaroJKMKHE18 fatcat:amo7u4j7hrdsfo3espt5kc6xyq

CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities

Tariq Ahmad, Allan Ramsay, Hanady Ahmed
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
In this paper we present our contribution to SemEval-2018, a classifier for classifying multi-label emotions of Arabic and English tweets.  ...  We attempted "Affect in Tweets", specifically Task E-c: Detecting Emotions (multi-label classification).  ...  Datasets for tweets are made available in three languages; Arabic, English and Spanish. We focus firstly on Arabic and then English because this links well with our existing work.  ... 
doi:10.18653/v1/s18-1030 dblp:conf/semeval/AhmadRA18 fatcat:rm5e64dh75fm7p53jmir2ujbfy

UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Pavel Přibáň, Tomáš Hercig, Ladislav Lenc
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish.  ...  This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018).  ...  -018 Data and Software Engineering for Advanced Applications.  ... 
doi:10.18653/v1/s18-1018 dblp:conf/semeval/PribanHL18 fatcat:htbyvf4hmvfrjjf4sesb4hkhyi

Combining Context-aware Embeddings and an Attentional Deep Learning Model for Arabic Affect Analysis on Twitter

Hanane Elfaik, El Habib Nfaoui
2021 IEEE Access  
In this paper, we address the problem of Arabic affect detection (multilabel emotion classification) by combining the transformer-based model for Arabic language understanding AraBERT and an attention-based  ...  In addition to the own unique features of tweets (shortness, noisiness, short length, etc.), the Arabic language is characterized by its agglutination and morphological richness.  ...  develops a multilabel emotion classification system to detect the emotions embedded in Arabic, Spanish and English tweets.  ... 
doi:10.1109/access.2021.3102087 fatcat:4y4ah7mxnzf7dfuducohavjboy

Emotion Analysis of Arabic Tweets during COVID-19 Pandemic in Saudi Arabia

Huda Alhazmi, Manal Alharbi
2020 International Journal of Advanced Computer Science and Applications  
We investigate how emotional perspective vary regarding lockdown ending in Saudi Arabia. We develop an emotion detection method to classify tweets into standard eight emotions.  ...  Furthermore, we present insights into the changes in the intensity of the emotions over time. Our finding shows that joy and anticipation are the most dominant among all emotions.  ...  Thus, there are not many studies on analyzing and detecting emotion in Arabic tweets.  ... 
doi:10.14569/ijacsa.2020.0111077 fatcat:g7ds2b3ymzdahb2shs4yqfzbom

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

Nora Alturayeif, Hamzah Luqman
2021 Applied Sciences  
We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet.  ...  of multi-dialect Arabic tweets with an F1-Micro score of 0.72.  ...  Acknowledgments: The authors would like to acknowledge the support provided by King Fahd University of Petroleum and Minerals (KFUPM) during this work.  ... 
doi:10.3390/app112210694 fatcat:2t7fqfmzr5ezvhr2ytcpynkliy

Emotion analysis of Arabic tweets using deep learning approach

Massa Baali, Nada Ghneim
2019 Journal of Big Data  
Thus, we present our approach used to classify emotions in Arabic tweets.  ...  The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent  ...  Availability of data and materials Dataset from SemiEval.  ... 
doi:10.1186/s40537-019-0252-x fatcat:ys6ndf4cdfarrlfwzkovvqsrm4

Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

Kashish Ara Shakil, Kahkashan Tabassum, Fawziah S. Alqahtani, Mudasir Ahmad Wani
2021 Applied Sciences  
A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons.  ...  We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city).  ...  Arabic English Emotion Lexicon (AEELex) Design AEELex is a bilingual emotion lexicon for extracting user emotions. This lexicon has been developed in Arabic and English language.  ... 
doi:10.3390/app11156846 fatcat:a5b7sgrznvey5plv75ej4zmdky

TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection

Anon George, Barathi Ganesh H. B., Anand Kumar M, Soman K P
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
Emotions are a way of expressing human sentiments. In the modern era, social media is a platform where we convey our emotions. These emotions can be joy, anger, sadness and fear.  ...  In the amount of digital language shared through social media, a considerable amount of data reflects the sentiment or emotion towards some product, person and organization.  ...  Datasets Dataset consists of tweets from three languages that are English, Spanish and Arabic. These are mainly focused on the emotions contained in it.  ... 
doi:10.18653/v1/s18-1050 dblp:conf/semeval/GeorgeBMP18 fatcat:zocgacaqzjbvlf6m3rjzbyista

Exploration of the best performance method of emotions classification for arabic tweets

Mohammed Abdullah Al-Hagery, Manar Abdullah Al-assaf, Faiza Mohammad Al-kharboush
2020 Indonesian Journal of Electrical Engineering and Computer Science  
This study aimed to explore the best performance scenarios in the classification of emotions conveyed through Arabic tweets.  ...  The active Arab presence on Twitter motivates many researchers to classify and analysis Arabic tweets from numerous aspects.  ...  Many researchers have studied emotions in English tweets, yet few of them have focused on emotions in Arabic tweets [15] .  ... 
doi:10.11591/ijeecs.v19.i2.pp1010-1020 fatcat:vcslbblgkzaf7beoczlcpoj72a
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