# Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text
## 3 Materials and Methods
**Frequency of negation in posts**: In CA-SN, the number of positive and negative targets affected by inverters is 229% more than those of CN-SA, this means that as the frequency of presence of inverters in posts increases, the number of incorrectly classified posts will increase and therefore a solution like ours will be needed to resolve such cases. In other words, the performance improvement will be appreciated when there is a significant part of posts containing inverters. 2. **Increase in the number of patterns**: as mentioned earlier, splitting inverters from their targets led to increasing the set of patterns without losing the original one. The number of patterns in SN increased by 4% while that of SA decreased by 3%. The increase in the number of patterns in SN led to higher performance when classifying SN. In con \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Before Treating Negation** & **After Treating Negation** \\ \hline
**CA—SN** & 0.73 & 0.93 \\
**CN—SA** & 0.73 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of negation on classification \(F1\)-score. trast, a decrease in the number of patterns of SA did not lead to a decrease in performance since, as we mentioned earlier, when splitting negated phrase and adding the target to its corresponding set, the algorithm will check the presence of any inverter before each pattern, and therefore the effect of the original negated phrase will be conserved. However, the decrease in the number of patterns was because the newly found patterns (after splitting) were already part of SA, so the set lost the negated phrases as a pattern and did not gain new ones. This did not affect performance. When talking about the Arabic language, there is no standard structure, especially with negation. The Arabic language has various structures for different parts of speech. Until now, the research in Arabic natural language processing is considered in its beginning stage. More work should be done. It is normal to have rule-based methods that are performing better than machine learning when dealing with negation. This depends on the feature extraction process and the size of the available corpus, and the variety of valence shifters. In this corpus, only negation was available. Negation was not discussed in many research that deal with machine learning. Although this research was applied to a corpus of posts collected from Facebook, the same approach can also be applied to any other social network. Twitter has an API that allows researchers to collect data in the form of tweets. Thus, the same work can be applied to the tweets corpus. The proposed work in this paper can add value to recommender systems and building user-interest profiles as in [25]. The authors in [25] were trying to discover the popularity of posts on Instagram. Negation can add value to this work and help enhance the results of popularity ranking. Before considering the popularity, Instagram posts should be classified. Negation can help classification techniques and allows the model they suggest to understand the semantics of the popular posts. Popular post can then be predicted once the post is posted on Instagram. Instagram can expect whether the post will go viral or not. In [26], the authors classify tweets into positive and negative tweets to use the classification in building a user-interest profile. The authors do not consider negation in their work. Thus, many lost tweets were either incorrectly classified or unclassified. Negation and valence shifters can enhance the results of the work done in [26]. ## 6 Conclusions

Negating Words have the tendency to invert the polarity of a word and to negate (or express the absence) of the term that comes after them. In this paper, we used Facebook posts to study the effect of negating words on sentiment polarity of a post. We highlighted few challenges that hinder using negating terms directly as classification features such as tokenization, odd negation, etc.