SANA : Sentiment Analysis on Newspapers comments in Algeria

TL;DR

This paper explores sentiment analysis of comments on Algerian newspaper websites, comparing classifiers and highlighting the impact of stemming, with promising results for future research in this domain.

Contribution

It introduces a new Algerian Arabic comments corpus and evaluates multiple classifiers, revealing the effectiveness of KNN and the significance of stemming in sentiment analysis.

Findings

KNN outperforms other classifiers in this domain.

Stemming significantly affects sentiment classification accuracy.

The study provides valuable resources for future Algerian sentiment analysis research.

Abstract

It is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment analysis or opinion mining is an emergent field who is the purpose is finding the behind phenomenon masked in opinionated texts. We are interested in our work by comments in Algerian newspaper websites. For this end, two corpora were used SANA and OCA. SANA corpus is created by collection of comments from three Algerian newspapers, and annotated by two Algerian Arabic native speakers, while OCA is a freely available corpus for sentiment analysis. For the classification we adopt Supports vector machines, naive Bayes and knearest neighbors. Obtained results are very promising and show the different effects of stemming in such domain, also knearest neighbors give important improvement comparing to other classifiers unlike similar works where SVM is the most dominant. From this study we observe the importance of dedicated resources and methods the newspaper comments sentiment analysis which we look forward in future works.