Sentiment analysis in multiple languages

The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The Entropy Weighted Genetic Algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of the key features. The proposed features and techniques are evaluated on a benchmark movie review data set and U.S. and Middle Eastern web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracy over 95 % on the benchmark data set and over 93 % for both the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all test beds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document level classification of sentiments