Investigation of the Feature Selection Problem for Sentiment Analysis in Arabic Language

Sentiment analysis, which is also known as opinion mining, can be defined as the process of the automatic detection of the attitude of an author towards a certain subject in textual contents. In this study we design and implement a document-level supervised sentiment analysis system for Arabic context and investigate its performance. We use three different feature extraction methods in order to generate three different datasets (unigrams, bigrams and trigrams) from the Opinion Corpus for Arabic (OCA). In order to find the optimal number of features and to obtain the best time performance in sentiment analysis, we employ two feature ranking methods (Information Gain based and Chi-Square based) and calculate the score of each feature with respect to the class labels. This feature ranking step selects only the features that are relevant to the class labels and removes the irrelevant features that cause unnecessary processing. Hence, it helps to increase the classification performance and reduce the processing time. Finally, we evaluate the performance of three standard classifiers for polarity on the previously generated unigram and bigram based data sets, namely Support Vector Machines, K-Nearest Neighbor and Decision Tree, known by their effectiveness over these types of datasets. In our study SVM classifier has showed superior classification performance compared to the other two classifiers. Our experimentation results also prove the effectiveness of the two feature selection methods we use in order to reduce the feature space of the generated datasets and provide higher classification performance.