Show simple item record

AuthorBaly, Ramy
AuthorHajj, Hazem
AuthorHabash, Nizar
AuthorShaban, Khaled Bashir
AuthorEl-Hajj, Wassim
Available date2021-01-27T11:06:56Z
Publication Date2017
Publication NameACM Transactions on Asian and Low-Resource Language Information Processing
ResourceScopus
ISSN23754699
URIhttp://dx.doi.org/10.1145/3086576
URIhttp://hdl.handle.net/10576/17512
AbstractAccurate sentiment analysis models encode the sentiment of words and their combinations to predict the overall sentiment of a sentence. This task becomes challenging when applied to morphologically rich languages (MRL). In this article, we evaluate the use of deep learning advances, namely the Recursive Neural Tensor Networks (RNTN), for sentiment analysis in Arabic as a case study of MRLs. While Arabic may not be considered the only representative of all MRLs, the challenges faced and proposed solutions in Arabic are common to many other MRLs. We identify, illustrate, and address MRL-related challenges and show how RNTN is affected by the morphological richness and orthographic ambiguity of the Arabic language. To address the challenges with sentiment extraction from text in MRL, we propose to explore different orthographic features as well as different morphological features at multiple levels of abstraction ranging from raw words to roots. A key requirement for RNTN is the availability of a sentiment treebank; a collection of syntactic parse trees annotated for sentiment at all levels of constituency and that currently only exists in English. Therefore, our contribution also includes the creation of the first Arabic Sentiment Treebank (ARSENTB) that is morphologically and orthographically enriched. Experimental results show that, compared to the basic RNTN proposed for English, our solution achieves significant improvements up to 8% absolute at the phrase level and 10.8% absolute at the sentence level, measured by average F1 score. It also outperforms well-known classifiers including Support Vector Machines, Recursive Auto Encoders, and Long Short-Term Memory by 7.6%, 3.2%, and 1.6% absolute respectively, all models being trained with similar morphological considerations. ACM
Languageen
PublisherAssociation for Computing Machinery
SubjectSentiment Analysis
Arabic Dialects
Text Classification
TitleA sentiment treebank and morphologically enriched recursive deep models for effective sentiment analysis in Arabic
TypeArticle
Issue Number4
Volume Number16
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record