# Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text
## 3 Materials and Methods
However, it is essential to note that these cases are the product of the analyzed corpora, and other cases exist in Arabic [16] but were not present. The following cases discuss the specific obstacles to inverters in sentiment analysis that the sample used in this work are illustrated. They are different from the valence shifters represented in the literature:  
1. **Tokenization**: When inverters with scope one are the adequate form some sentiment analysis. Our corpora contradicted this norm because spelling rules were not followed in DA on social media. Consider the negated pattern _'s_'s_'s; ideally, space should separate the subject from the negative pattern. As there is no space, this phrase will not be properly tokenized. Thus, it will not be treated in a proper way. Unluckily, improper tokenization is frequently available in DA. One solution would be to search for inverters prior to tokenization and thus split the negation from the subject. This approach, however, would raise other challenges at the tokenization stage. For example, legitimate cases where a negation may be falsely identified based on lexical matching. Consider the word _'s_'s_'(legal): negation sensitive tokenization might result in a classifier detecting _'s_'s_' as a positive pattern, preceded by an inverter _'s_'s_'s, an incorrect inversion. This process is challenging when an inverter is a prefix. For example, the verb _'s_'(liked him), can be negated by adding the letter _'s_' (M) \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Arts Corpus** & **News Corpus** \\ \hline
**Negative** & 224 & 230 \\
**Positive** & 233 & 236 \\
**Dual** & 151 & 161 \\
**Spam** & 197 & 193 \\
**Neutral** & 195 & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of Posts in the Dataset. as a prefix to the word, leading to _expa_, but the exact word has another meaning of admirer or fan which is a positive meaning. Another example would be _expa_ the word may mean lover or did not love at the same time depending on context. In MSA or with diacritized text, the problem can be resolved. However, since DA cannot be diacritized, other solutions are needed. Smarter tokenization may help eliminate such cases by splitting prefixes and checking for the legitimacy of the remaining string as a standalone Arabic word. For example, _expa_, _expa_, will be split into _expa_, _expa_ but after checking the words before it, splitting the word may be disregarded if it turned out the _expa_ is an adjective to what comes before it. However, such \"solutions\" have their own problems of additional complexity and still legitimate cases by wrongly interpreted. For example, the letter _expa_ is an inverter, yet there are plenty of words that start with this letter without being inverted. 2. **Fake Inverters**: The strings representing inverters have other usages not related to negation. Consider the phrase _expa_ (how beautiful she is): the phrase consists of a string used usually for negation followed by a positive word. If MSA was used, searching for patterns that belong to the morphological group used usually for comparison _expa_ would resolve the ambiguity. However, since such tools do not exist for DA, and if we assumed that they are available, the spelling irregularities would hinder the usage of such tools. For example, _expa_ (how beautiful) is a positive pattern that is usually used to praise the beauty of an object, yet this pattern is not written \(\dagger\) (A) as it should be. What complicates the problem is when the exact \"fake\" negation cases appear in legitimate negation scenarios. Consider the phrase _expa_ (not a beautiful voice at all). This is a positive pattern that is preceded by an inverter that makes it negative. Another example is _expa_ (not the most beautiful voice). The positive sentence is preceded by an inverter that makes it negative. There is an important observation that many targets of the fake inverters start with the letter \(\dagger\) (A).