# Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text
## 2 Background
### Valence Shifters
For example, \"mean\" is a negative word, but when used in \"Although he is mean, he treats birds in a nice way,\" it has a positive meaning. * Discourse structure: Sentences may consist of two parts: dominant and illustrative. The illustrative part supports the dominant part such that when the dominant part is opinionated, the illustrative part intensifies its sentiment. Consider the sentence \"He is a great hunter. He caught five animals today\". The first part of the sentence contains the word \"great,\" so it is positive and opinionated. However, the second part of the sentence is neutral and intensifies the first sentence. * Reported speech: some sentences like \"he said that the episode is nice\" do not mean that the author agrees on the positive sentiment. However, if it is \"he said that the episode is nice and I totally agree\" is supported by a phrase to give positive sentiment. Thus, \"I agree\" and \"I do not agree\" are keywords that can be used in reported speech and may change the sentiment of a sentence. The fact that negation is a challenge to sentiment analysis in Arabic is widely recognized [14], and that few works mention the challenges of negation. In [15], the author reports that negation makes classification more challenging. In [16], the authors present three contributions related to negation: (1) presenting an approach that identifies the negation's scope with the presence of inverter such as none, not, or barely, (2) presenting a methodology that identifies the polarity of a fragment of a sentence that has an inverter, and (3) the scope of negation concept is introduced in a system of opinion retrieval. Similarly, in [17], the author highlights the inaccuracy of some detection techniques that neglect the presence of negation words in the Arabic language sentences. She claims that some classification techniques consider negation words as stop words; thus, they are removed. Consequently, this will lead to false detection of the real meaning of a sentence because negation reveals the truth of one's opinion. Accordingly, the author proposed an approach in which the negation entailment is significantly showing a higher detection accuracy. Lexicon-based approaches in [18; 19] are based on the effect of negation using a list of negating terms and shift or transform valence in various ways. Based valence on accumulative values with negative words decrease the value (for instance, \"not\" has the impact of \"\(-4\)\"). This approach means that a negation would lower the valence but not fully invert it. For example, if \"beautiful\" is weighted +5, then \"not awesome\" will not be weighted \(-5\). However, the result of adding \(-4\) is \(1\). Thus, \"not beautiful\" still has a positive polarity albeit less than \"beautiful\". By contrast, in [6], the frequency of inverters in a post was used as one of the features in the classification; however, the behaviour of inverters and their exact effect on the classification process was not addressed. In [20], it was assumed that negating terms always precede the targets directly, and, hence, the targets' polarity was inverted whenever preceded by an inverter. Clearly, word order and the scope of inverters are strongly linked to the form of a specific language. For instance, English inverters are commonly preceding their targets. Our inverter scope supports this, finding that in 96.5% of the cases, the inverters had a scope of one, which means that they directly preceded the target, as expected according to the Arabic grammar rules [21]. This paper contributes to the above body of work by further analyzing valence shifter in sentiment classification for Arabic and DA. We focus on inverters. We follow the assumption mentioned in [6; 20] in working with negation focusing upon inverters whose targets have scope one.