A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

Sentiment analysis for helpful reviews prediction

Nowadays, every purchase we plan can be alleviated by the advice of those that tried in the past the given product. As more and more reviews are available, it would be practical to filter the relevant reviews not only to speed up the decision process but also to improve it. Gathering only the helpful reviews would reduce information processing time and save effort. To develop this functionality we need reliable prediction algorithms to classify and predict new reviews as helpful or not, even if the review has not been voted yet. In this paper, we propose a new approach which predicts reviews helpfulness based on sentiment analysis. Our approach focused on sentiment features such as the degree of positivity and the degree of negativity, in addition to the simplistic counts computed directly from reviews. It also extracts emotions dimension by means of emotion lexicon. We proposed a solution to internally construct an emotion lexicon in order to overcome challenges of invented terms, domain dependency, and spelling mistakes. We applied the proposed approach to Facebook pages of six medical products. We obtain a prediction accuracy of 97.95% through SVM algorithm. We found that sentiment degree and sadness emotion are the most decisive sentiment features to predict review helpfulness. The word count and frequencies are important as they reflect the richness and the seriousness of the review, but sentiment and emotions are more decisive as they engage and influence users.Published versio

STRPRO tool for Manipulating Stratified Programs Based on SEPN

Abstract- Negation is useful in the majority of the real world applications. However, its introduction leads to semantic and canonical problems. SEPN nets are well adapted extension of predicate nets for the definition and manipulation of stratified programs. This formalism is characterized by two main contributions. The first concerns the management of the whole class of stratified programs. The second contribution is related to usual operations optimization (maximal stratification, incremental updates …). We propose, in this paper, useful algorithms for manipulating stratified programs using SEPN. These algorithms were implemented and validated with STRPRO tool

Analyse du statut socio-économique des usagers de Twitter

International audienc