  In this paper, we propose our enhanced approach to create a dedicated corpus
for Algerian Arabic newspapers comments. The developed approach has to enhance
an existing approach by the enrichment of the available corpus and the
inclusion of the annotation step by following the Model Annotate Train Test
Evaluate Revise (MATTER) approach. A corpus is created by collecting comments
from web sites of three well know Algerian newspapers. Three classifiers,
support vector machines, na{\"i}ve Bayes, and k-nearest neighbors, were used
for classification of comments into positive and negative classes. To identify
the influence of the stemming in the obtained results, the classification was
tested with and without stemming. Obtained results show that stemming does not
enhance considerably the classification due to the nature of Algerian comments
tied to Algerian Arabic Dialect. The promising results constitute a motivation
for us to improve our approach especially in dealing with non Arabic sentences,
especially Dialectal and French ones.
