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Öğe ANALYSIS OF DATA PRE-PROCESSING METHODS FOR SENTIMENT ANALYSIS OF REVIEWS(Agh Univ Science & Technology Press, 2019) Parlar, Tuba; Ozel, Selma Ayse; Song, FeiThe goals of this study are to analyze the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods (and their combinations) are effective for English as well as for an agglutinative language like Turkish. We also try to answer the research question of whether there are any differences between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis. We find that the performance results for the English reviews are generally higher than those for the Turkish reviews due to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.Öğe Comparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter Data(Ieee, 2017) Parlar, Tuba; Sarac, Esra; Ozel, Selma AyseThe Internet and social media provide a major source of information about people's opinions. Due to the rapidly growing number of online documents, it becomes both time-consuming and hard task to obtain and analyze the desired opinionated information. Sentiment analysis is the classification of sentiments expressed in documents. To improve classification perfromance feature selection methods which help to identify the most valuable features are generally applied. In this paper, we compare the performance of four feature selection methods namely Chi-square, Information Gain, Query Expansion Ranking, and Ant Colony Optimization using Maximum Entropi Modeling classification algorithm over Turkish Twitter dataset. Therefore, the effects of feature selection methods over the performance of sentiment analysis of Turkish Twitter data are evaluated. Experimental results show that Query Expansion Ranking and Ant Colony Optimization methods outperform other traditional feature selection methods for sentiment analysis.Öğe Interactions Between Term Weighting and Feature Selection Methods on the Sentiment Analysis of Turkish Reviews(Springer International Publishing Ag, 2018) Parlar, Tuba; Ozel, Selma Ayse; Song, FeiTerm weighting methods assign appropriate weights to the terms in a document so that more important terms receive higher weights for the text representation. In this study, we consider four term weighting and three feature selection methods and investigate how these term weighting methods respond to the reduced text representation. We conduct experiments on five Turkish review datasets so that we can establish baselines and compare the performance of these term weighting methods. We test these methods on the English reviews so that we can identify their differences with the Turkish reviews. We show that both tf and tp weighting methods are the best for the Turkish, while tp is the best for the English reviews. When feature selection is applied, tf * idf method with DFD and chi(2) has the highest accuracies for the Turkish, while tf * idf and tp methods with chi(2) have the best performance for the English reviews.Öğe A New Feature Selection Method for Sentiment Analysis of Turkish Reviews(Ieee, 2016) Parlar, Tuba; Ozel, Selma AyseSentiment analysis identifies people's opinions, sentiments about a product, a service, an organization, or an event. Because of huge review documents, researchers explore different feature selection methods that aim to eliminate non valuable features. However, not much work has been done on feature selection methods for sentiment analysis of Turkish reviews. In this study, we propose a new feature selection method called Query Expansion Ranking that is based on query expansion term weighting methods, which are used in Information Retrieval domain to determine the most valuable terms for query expansion. We compare Query Expansion Ranking with Chi Square method, which is a well-known and successful feature selector, and Document Frequency Difference which is a feature selection method proposed for sentiment analysis of English reviews. Experiments are conducted on four Turkish product review datasets that are book, DVDs, electronics, and kitchen appliances reviews by using a supervised machine learning classification method, namely Naive Bayes Multinomial classifier. We show that our new proposed method improves sentiment analysis performance in terms of classification accuracy and time. In the experimental evaluation, we also show that our new feature selector improves classification accuracy better than Chi Square, and Document Frequency Difference methods.Öğe QER: a new feature selection method for sentiment analysis(Korea Information Processing Soc, 2018) Parlar, Tuba; Ozel, Selma Ayse; Song, FeiSentiment analysis is about the classification of sentiments expressed in review documents. In order to improve the classification accuracy, feature selection methods are often used to rank features so that non-informative and noisy features with low ranks can be removed. In this study, we propose a new feature selection method, called query expansion ranking, which is based on query expansion term weighting methods from the field of information retrieval. We compare our proposed method with other widely used feature selection methods, including Chi square, information gain, document frequency difference, and optimal orthogonal centroid, using four classifiers: na < ve Bayes multinomial, support vector machines, maximum entropy modelling, and decision trees. We test them on movie and multiple kinds of product reviews for both Turkish and English languages so that we can show their performances for different domains, languages, and classifiers. We observe that our proposed method achieves consistently better performance than other feature selection methods, and query expansion ranking, Chi square, information gain, document frequency difference methods tend to produce better results for both the English and Turkish reviews when tested using na < ve Bayes multinomial classifier.
