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2018 | OriginalPaper | Chapter

Sentiment Analysis in Arabic Twitter Posts Using Supervised Methods with Combined Features

Authors : Rihab Bouchlaghem, Aymen Elkhelifi, Rim Faiz

Published in: Computational Linguistics and Intelligent Text Processing

Publisher: Springer International Publishing

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Abstract

With the huge amount of daily generated social networks posts, reviews, ratings, recommendations and other forms of online expressions, the web 2.0 has turned into a crucial opinion rich resource. Since others’ opinions seem to be determinant when making a decision both on individual and organizational level, several researches are currently looking to the sentiment analysis.

In this paper, we deal with sentiment analysis in Arabic written Twitter posts. Our proposed approach is leveraging a rich set of multilevel features like syntactic, surface-form, tweet-specific and linguistically motivated features. Sentiment features are also applied, being mainly inferred from both novel general-purpose as well as tweet-specific sentiment lexicons for Arabic words.

Several supervised classification algorithms (Support Vector Machines, Naive Bayes, Decision tree and Random Forest) were applied on our data focusing on modern standard Arabic (MSA) tweets. The experimental results using the proposed resources and methods indicate high performance levels given the challenge imposed by the Arabic language particularities.

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previous chapter Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis

next chapter Interactions Between Term Weighting and Feature Selection Methods on the Sentiment Analysis of Turkish Reviews

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http://twitter4j.org/en/index.html.

http://nlp.stanford.edu/.

http://textometrie.ens-lyon.fr/?lang=en.

https://wordnet.princeton.edu.

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Title: Sentiment Analysis in Arabic Twitter Posts Using Supervised Methods with Combined Features
Authors: Rihab Bouchlaghem
Aymen Elkhelifi
Rim Faiz
Publisher: Springer International Publishing
Book: Computational Linguistics and Intelligent Text Processing
Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-75487-1_25

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