# Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text
###### Abstract  
With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors' opinions about specific subjects, such as people's opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word's polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text. social networks; sentiment analysis; Arabic language; negation +
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for tools to use [6]; furthermore, common informal use of sarcasm and negation makes accurate opinion identification very hard. Many research articles have explored the need for sentiment analysis in the Arabic language due to its popularity, particularly in most social media platforms. For example, in [7], the authors focused on studying the Arabic sentiment rather than English sentiments because the Arabic language has fewer resources than those available for the English language. Therefore, they have proposed an Arabic dataset collected from many tweets in different Arabic dialects. Their 151,000-opinion dataset was labeled as either positive or negative opinions. Their dataset was then used by many machine learning techniques showing that the ridge regression outperforms all other techniques with an accuracy of 99.9%. Similarly, the authors in [8] proposed a new approach to generate sentiment models using different machine learning techniques. Their approach was elaborated on using different social media platforms. In addition, they have handled some Arabic slang vocabulary and shorthand writings. They classified the sentiments into five categories from highly positive to highly negative, with 'No sentiment' as the middle one. The results of their approach showed high classification accuracy. In [9], the authors focused on tracking individuals' sentiments towards news in the Arab world. They built a corpus that contains around 7200 tweets that are manually annotated. A tracking system is presented in their study, considering sentiment classification and named entity recognition techniques. After applying the dataset to different machine learning classifiers, the authors showed that tracking people's sentiments is well-performing while exploring important entities extracted from social networks in different Arab regions. Accordingly, their technique helps predict the future sentiment of new entities. The authors in [10] have presented another approach for sentiment analysis. They have created their ontology based on their country tourism. They worked on the Twitter platform to collect tweets using POS tagger. The obtained entities have been compared to the specific domain ontology. As a result, they extracted the essential feature from their collected data. The latter can then be combined with machine learning techniques to improve sentiment analysis of their study.