Abstract:
Sentiment analysis aims to determine the polarity that is embedded
in people comments and reviews. Sentiment analysis is important for
companies and organisations which are interested in evaluating their
products or services. The current paper deals with sentiment analysis in
Arabic reviews. Three classifiers were applied on an in-house developed
dataset of tweets/comments. In particular, the Na?ve Bayes, SVM and
K-nearest neighbour classifiers were employed. This paper also addresses the
effects of term weighting schemes on the accuracy of the results. The binary
model, term frequency and term frequency inverse document frequency were
used to assign weights to the tokens of tweets/comments. The results show that
alternating between the three weighting schemes slightly affects the accuracies.
The results also clarify that the classifiers were able to remove false examples
(high precision) but were not that successful in identifying all correct examples
(low recall).