الفهرس 
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Abstract
Through the state-of-the-art digitalization, we can see a massive
growth in user-generated content on the web that provides feedback
from people on a variety of topics. However, manually managing largescale
user feedback would be a hard task and a waste of time. Therefore,
the concept of opinion mining or sentiment analysis is emerged. Opinion
mining is a computerized study of individuals’ feelings and opinions
about an entity or product. Opinion mining is difficult because the
users’ opinions or reviews are mostly unstructured text, lower quality,
noisy, and spam. Thus, there are several challenges facing the opinion
mining and causing poor classification performance. Some of these challenges
are multiple languages of user reviews, fake reviews, manipulation
of emoticons, implicit aspects, spam aspects, and negative words that
change the class label of opinion words. In this thesis a semantic-based
aspect level opinion mining (SALOM) model is proposed. In order to
address some of the previously mentioned challenges. Whereas, SALOM
involves the semantic similarity measure and Wordnet ontology to extract
the product related domain aspects. Moreover, it considers other
types of product aspects such as aspect-related synonym, hyponym, and
hypernym. Not only that, but the proposed model also considers the
reviews that contain product aspects only. In addition, SALOM model
considers negation words that affect the performance of the opinion mining
process. Five different datasets are used to evaluate the proposed
SALOM model. SALOM achieved promising results compared to other
methods. Where, the performance reached 91.6% for accuracy, 93% for
recall, 95.8% for precision, and 93.3% for f-measure.