# Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
## 4 Methodology
### Polarity Classification  
In this work, we propose to perform polarity classification before the automatic summarization to mimic the human way of writing summaries. Traditionally, it is assumed that user sentiment, expressed in text reviews, is reflected by the score rating. However, by skimming the reviews, we noticed that sometimes the sentiment of the review disagrees with the score value given by the user. A few examples of these reviews are presented in Table 3. To find whether the number of contradicting reviews is significant, we calculated the percentage of these reviews for each hotel, the result of this step is presented in Table 4. The result shows that the disagreement between the rating-based and content-aware sentiments is 18.7% in the first hotel and 5.35% in the second. This inaccuracy could affect the performance of the sentiment classifier negatively. Thus, to provide more accurate labeling, we considered text mining approaches to find the sentiment of each review. Formally, we have applied the following five methods:  
1. Classifying is based on the original users' rating, which includes ratings from 1 (lowest rating) to 5 (highest rating).
2. Classifying based on users' positive, negative, or neutral ratings. To perform this approach, the users' ratings of 5 and 4 were considered positive, three as neutral, and 2 and 1 as negative reviews.
3. Classifying based on sentiment analysis of all the reviews in the dataset, i.e., 409,312 reviews for the 1173 hotels. This classification was also based on the users' original ratings; however, it considers the linguistic features and patterns modeled via  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Before Preprocessing** & **After Preprocessing** & **Translation** \\ \hline \hline
\begin{tabular}{l} _Journal_ \\ _Journal_  
the training of deep learning architecture. This procedure is illustrated in Algorithm 1 below. The deep learning architecture is discussed in the following subsection.
4. This approach is similar to the previous approach and is based on sentiment analysis of all reviews in the dataset using deep learning modeling. However, the classification model classifies the reviews into positive, negative, and neutral using the same method described above for the second approach.
5. Instead of using the rating score for sentiment in this experiment, we relabeled each review automatically using a pretrained model Mazajak [37], a deep learning model trained on Arabic Twitter data using CNN-LSTM models, more about Mazajak is presented below. This API labeled each review as positive, negative, or neutral.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hotel** & **Total Reviews** & **Disagreements** & **Percentage** & **Rating Distribution** \\ \hline \hline
1 & 224 & 42 & 18.7\% \\ \hline
2 & 224 & 12 & 5.35\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Disagreements between the original reviews rating and the rating of the developed deep learning-based sentiment analysis model (left) and the rating distributions of the two hotels (right).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Review** & **Translation** & \begin{tabular}{c} **Rating Based on the** \\ **Proposed Deep** \\ **Learning Model** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **Rating** \\ \end{tabular} \\ \hline \hline \
\begin{tabular}{c} _With the experience, great \\ place, everything was great \\ \end{tabular} & 5 & 2 \\
\begin{tabular}{c} _Global_ \\ _Global