# AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus
## 2 Related Work
### Sentiment Classification  
The sentiment classification is the task of classifying a document into positive, negative, or neutral classes [38]. There are different sentiment classification approaches and tools used for the sentiment classification task on the document level that considers the whole document as a basic information unit. In this part, we will describe the approaches, features, and techniques used along with types of testing and validation of the existing sentiment corpora with a focus on the Arabic sentiment corpora.  
From the revised literature, it was proved that machine learning approaches (e.g., support vector machine, naive Bayes, and logistic regression) are the classifiers used for sentiment classifications, as shown in Table 2. These classifiers are suitable for the case of Twitter data [4; 15; 21; 24; 25; 28; 33; 34] and YouTube data [39].  
Many studies used deep learning models to recognize and understand data such as text, audio, and images. Long short-term memory (LSTM) and convolutional neural network (CNN) deep learning classifiers were used in [17]. They used both classifiers with a huge amount of training data to achieve better results. In [26], Bi-LSTM and LSTM achieved 94% and 92% accuracy for a dataset of 32,063 tweets, respectively. CNN and LSTM are two deep learning models that were used in [33]. The models achieved F-measures of 95% and 93%, respectively, by using a word2 vec pre-trained embedding as input features for both models. An online system for Arabic sentiment analysis called \"Mazakak\" was presented in [41]. The system was developed based on a CNN followed by an LSTM deep learning model. It achieved state-of-the-art results on two benchmark datasets, SemEval  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classification** & **Approaches** & **Features/Techniques** & **Studies** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} } & Support Vector Machines (SVM), & Term & [4; 10; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] & Frequency—Inverse \\  & Decision Tree (DT), Naive Bayes & Document Frequency & 13,15; 16; 19; 22; 26; 27; 29; 32; 33; 34] \\ \multirow{4}{*}{
\begin{tabular}{c} Deep \\ Learning \\ \end{tabular} } & Long Short-Term Memory (LSTM), & & \\  & Bidirectional Long Short-Term & & \\  & Memory (B-LSTM), and & & \\  & Convolutional Neural Network & & \\  & (CNN) & & \\ \cline{1-1} \cline{3-5}  & & Lexicon Terms & [10; 13; 30] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sentiment classification approaches: machine/deep learning classifiers commonly used in performing Arabic sentiment classification.  
2017 and ASTD. The proposed approaches represent documents on counting, N-grams, TF-IDF, and word embeddings features.  
Lexicon-based approaches were also used to perform sentiment analysis. In [42], an Arabic lexicon was constructed and combined with a named entity recognition (NER) system to show the importance of including NER in the process of sentiment analysis. The experimental results, on different Arabic sentiment corpora, obtained by using NER outperformed the results obtained without using NER. NileULex is an Arabic sentiment lexicon for modern standard Arabic and Egyptian dialect [43]. The lexicon was used over two manually annotated datasets to ensure that the developed lexicon is useful for Arabic sentiment analysis. The authors reported that the use of their lexicon, which is manually constructed and annotated, gives better results in comparison with the translated or automatically constructed lexicons. In [44], the sentiment polarity was calculated by looking for the polarity of each term of the message in the constructed lexicon. They performed several preprocessing steps including tokenization, normalization, repeat letters and stop words removal, light stemming, and negation and intensification handling. Two Arabic sentiment corpora were used to perform classification experiments, and the best accuracy result was 70%.