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IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines [article]

Soroush Javdan, Taha Shangipour ataei, Behrouz Minaei-Bidgoli
2020 arXiv   pre-print
Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict  ... 
arXiv:2007.12733v1 fatcat:ehgz5engqvhl7ao2gwakchdupq

Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian [article]

Taha Shangipour Ataei, Kamyar Darvishi, Soroush Javdan, Behrouz Minaei-Bidgoli, Sauleh Eetemadi
2019 arXiv   pre-print
Due to the increased availability of online reviews, sentiment analysis had been witnessed a booming interest from the researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e. aspect-based sentiment analysis). Most of the available data resources were tailored
more » ... English and the other popular European languages. Although Persian is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of public dataset on aspect-based sentiment analysis for Persian. This paper provides a manually annotated Persian dataset, Pars-ABSA, which is verified by 3 native Persian speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827 neutral data samples from 5,602 unique reviews. Moreover, as a baseline, this paper reports the performance of some state-of-the-art aspect-based sentiment analysis methods with a focus on deep learning, on Pars-ABSA. The obtained results are impressive compared to similar English state-of-the-art.
arXiv:1908.01815v3 fatcat:smkftrskmrfz7oxu23tk3y5vzm

Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection

Taha Shangipour ataei, Soroush Javdan, Behrouz Minaei-Bidgoli
2020 Proceedings of the Second Workshop on Figurative Language Processing   unpublished
Sarcasm is a type of figurative language broadly adopted in social media and daily conversations. The sarcasm can ultimately alter the meaning of the sentence, which makes the opinion analysis process error-prone. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and aspect-based sentiment analysis approaches in order to extract the relation between context dialogue sequence and response and determine whether or not the response is sarcastic. The
more » ... performing method of ours obtains an F1 score of 0.73 on the Twitter dataset and 0.734 over the Reddit dataset at the second workshop on figurative language processing Shared Task 2020.
doi:10.18653/v1/2020.figlang-1.9 fatcat:2suvbhiq3nehbiyqftse4xjpbm

IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using Deep Neural Networks and Linear Baselines

Soroush Javdan, Taha Shangipour ataei, Behrouz Minaei-Bidgoli
2020 Proceedings of the Fourteenth Workshop on Semantic Evaluation   unpublished
Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict  ... 
doi:10.18653/v1/2020.semeval-1.170 fatcat:hnvqusjncfhglhwe7mtggiyfba

A Report on the 2020 Sarcasm Detection Shared Task [article]

Debanjan Ghosh and Avijit Vajpayee and Smaranda Muresan
2020 arXiv   pre-print
Shangipour ataei, Soroush Javdan, and Behrouz Minaei-Bidgoli. 2020. Applying Transformers and aspect-based sentiment analysis approaches on sarcasm detection.  ...  Convolution and dense layers over this summarized context representation and BERT encoding of response make up the final classifier. taha (ataei et al., 2020) : Reported experiments comparing SVM on character  ... 
arXiv:2005.05814v2 fatcat:ybshirn2rjbutmi744lk34kgpy

A Report on the 2020 Sarcasm Detection Shared Task

Debanjan Ghosh, Avijit Vajpayee, Smaranda Muresan
2020 Proceedings of the Second Workshop on Figurative Language Processing   unpublished
Taha Shangipour ataei, Soroush Javdan, and Behrouz Minaei-Bidgoli. 2020. Applying Transformers and aspect-based sentiment analysis approaches on sarcasm detection.  ...  Convolution and dense layers over this summarized context representation and BERT encoding of response make up the final classifier. taha (ataei et al., 2020) : Reported experiments comparing SVM on character  ... 
doi:10.18653/v1/2020.figlang-1.1 fatcat:4swtljuke5av5ppfc7cwu27sxm