# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 4 Research Methodology
### Feature Extraction
SentiNet ([[https://sentic.net/](https://sentic.net/)]([https://sentic.net/](https://sentic.net/)), accessed on 12 January 2023) [75]: SenticNet is a concept-level lexicon that utilizes denotative and connotative information-associated concepts from the WordNet ([[https://wordnet.princeton.edu/](https://wordnet.princeton.edu/)]([https://wordnet.princeton.edu/](https://wordnet.princeton.edu/)), accessed on 12 January 2023) lexical database to perform emotion recognition. It is employed to extract mood tags at the word level. It covers eight emotions: anger, calmness, eagerness, disgust, fear, joy, pleasantness, and sadness. Additionally, we can extract negative and positive sentiment tags for each word in a sentence. 2. NRC Emotion Lexicon ([[http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm](http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm)]([http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm](http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm)), accessed on 12 January 2023): The NRC emotion lexicon is used to assign availability statuses to the eight emotions based on the Plutchik model, namely, anger, trust, disgust, fear, joy, sadness, surprise, and anticipation, as well as sentiment. Two Python packages are utilized to extract emotions: the LeXmo and NRCLex packages. The method we used is straightforward. First, we concatenated all the replies or news posts about a specific event; then, if certain words existed in a sentence, the sentence was considered to reflect a particular sentiment or emotion. For example, a statement containing the word \"happy\" expresses happiness and joy, while a sentence containing the word \"scare\" expresses the emotion of fear. More formally, given textual content with the length \(L\), W = [w\({}_{1}\), w\({}_{2}\), \(\ldots\), w\({}_{i}\), \(\ldots\), w\({}_{L}\)], where w\({}_{i}\) is the \(i\)th word in the text. We aimed to extract emotions related to emotional words E = [e\({}_{1}\), e\({}_{2}\), \(\ldots\), e\({}_{i}\)] that convey a certain emotion e. Then, we calculated the emotion frequency and normalized it by sentence length to obtain a feature value representing the intensity of that emotion in the given text. This process was applied to all emotions to create a feature vector for the entire text. We also used two publicly available pre-trained models that were specifically trained for the Arabic language to derive emotion categories and sentiments: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Feature** & **Type** & **Description** \\ \hline \multirow{8}{*}{Content-based features} & Is translator & Boolean & Whether account has a translator \\ \cline{2-4}  & Timespan & Float & Difference in years between account creation and tweet posted \\ \cline{2-4}  & Promotable & Boolean & Whether account is promotable \\ \cline{2-4}  & Normal followers & Boolean & Number of normal followers \\ \hline \multirow{8}{*}{Content-based features} & Retweet count & Integer & Total number of post retweets \\ \cline{2-4}  & Reply count & Integer & Total number of post replies \\ \cline{2-4}  & Favorite count & Integer & Total number of post favorites \\ \cline{2-4}  & URL presence & Boolean & Whether tweets have a URL \\ \cline{2-4}  & Question mark presence & Boolean & Whether tweets have a question mark \\ \cline{2-4}  & Exclamation mark presence & Boolean & Whether tweets have an exclamation mark \\ \cline{2-4}  & Media presence & Boolean & Whether tweets have media videos or images \\ \cline{2-4}  & Hashtag presence & Boolean & Whether tweets have hashtags \\ \cline{2-4}  & Word length & Integer & Tweet length in char \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._1. AraNet tools [36]: AraNet tools support a wide variety of Arabic NLP tasks, including dialect, emotion, and irony prediction. Built on BERT architecture, AraNet provides state-of-the-art performance for these tasks. It covers eight emotions: sadness, anticipation, surprise, anger, fear, happiness, disgust, and trust. 2.