Hybrid Deep Learning for Sentiment Polarity Determination of Arabic Microblogs

In this study, we investigate various deep learning models based on convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) recurrent neural networks for sentiment analysis of Arabic microblogs. Unlike English, the Arabic language has several specifics which complicate the process of feature extraction by traditional methods. We adopted a neural language model created at Google, known as word2vec, for vectorizing text. We then designed and evaluated several deep learning architectures using CNN and LSTM. The experiments were run on two publicly available Arabic tweets datasets. Promising results have been attained when combining LSTMs and compared favorably with most related work.

[1]  Mahmoud Al-Ayyoub,et al.  A prototype for a standard arabic sentiment analysis corpus , 2016, Int. Arab J. Inf. Technol..

[2]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[3]  Abdelkamel Tari,et al.  Data and Text Mining Techniques for Classifying Arabic Tweet Polarity , 2016 .

[4]  Rehab Duwairi,et al.  Detecting sentiment embedded in Arabic social media - A lexicon-based approach , 2015, J. Intell. Fuzzy Syst..

[5]  Mahmoud Al-Ayyoub,et al.  Evaluating SentiStrength for Arabic Sentiment Analysis , 2016, 2016 7th International Conference on Computer Science and Information Technology (CSIT).

[6]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[7]  Mahmoud Al-Ayyoub,et al.  Arabic sentiment analysis: Lexicon-based and corpus-based , 2013, 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).

[8]  Yelong Shen,et al.  Learning semantic representations using convolutional neural networks for web search , 2014, WWW.

[9]  Pengfei Duan,et al.  Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification , 2016, COLING.

[10]  Nazlia Omar,et al.  A Comparative Study of Feature Selection and Machine Learning Algorithms for Arabic Sentiment Classification , 2014, AIRS.

[11]  Heider A. Wahsheh,et al.  Arabic sentiment polarity identification using a hybrid approach , 2015, 2015 6th International Conference on Information and Communication Systems (ICICS).

[12]  Verena Rieser,et al.  An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis , 2014, LREC.

[13]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[14]  Lixin Tao,et al.  Word embeddings for Arabic sentiment analysis , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[15]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[16]  Bashar Al Shboul,et al.  Multi-way sentiment classification of Arabic reviews , 2015, 2015 6th International Conference on Information and Communication Systems (ICICS).

[17]  Verena Rieser,et al.  iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases , 2016, *SEMEVAL.

[18]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[19]  Samhaa R. El-Beltagy,et al.  Building Large Arabic Multi-domain Resources for Sentiment Analysis , 2015, CICLing.

[20]  Hazem M. Hajj,et al.  Deep Learning Models for Sentiment Analysis in Arabic , 2015, ANLP@ACL.

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[23]  El-Sayed M. El-Alfy,et al.  Using Word Embedding and Ensemble Learning for Highly Imbalanced Data Sentiment Analysis in Short Arabic Text , 2017, ANT/SEIT.

[24]  Saif Mohammad,et al.  How Translation Alters Sentiment , 2016, J. Artif. Intell. Res..

[25]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[26]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[27]  Amir F. Atiya,et al.  ASTD: Arabic Sentiment Tweets Dataset , 2015, EMNLP.