Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis  
[PERSON]  
1 College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea; [EMAIL_ADDRESS]  
1 College of Software, Sungkyunkwan University, Suwon 440-746, Korea  
2  
[PERSON]  
2 College of Software, Sungkyunkwan University, Suwon 440-746, Korea
(feature), which is decided according to its importance. Finally, a classification algorithm such as Naive-Bayes, Decision Tree, or Support Vector Machine is used to classify the tested document into a predefined category [6]. There exist some issues confronting TC. The first issue is how to effectively select useful features from the feature space. As the training data are usually massive, reducing the dimension of the feature space contributes to the saving of a significant amount of computational cost. The second one is the decision of feature weight. [PERSON] [8] firstly proposed the notion of Supervised Term Weighting (STW), where the terms are weighted considering the information of the category of the training dataset [9]. Unlike feature selection, where all the selected features are treated equally, the importance of the features serves as a criterion for determining the weights of the features. Extensive studies have been conducted for feature weighting with TC, while the method based on the Document Frequency (DF) is the simplest one. It is effective with large-scale corpus due to the simplicity, but overemphasizes the role of the appearance of the terms in deciding the weights, and ignores the terms of multiple appearances. With Term Frequency (TF), a weight is assigned to each term based on its frequency in the document instead of binary representation. With DF and TF, the importance of the terms is measured based on the appearance and frequency, respectively, which are the basic factors employed by various existing feature weighting approaches. Term Frequency and Inverse Document Frequency (TF-IDF) [2] reflects the two factors in deciding the weights to achieve good performance in various situations, and it is therefore widely used. Part-of-Speech feature-based Weighting (PSW) [10] is a recently introduced method that aggregates the terms into groups and weighs them in accordance with their inter-group importance. It performs well in many cases of sentiment analysis, while the clustering strategy significantly affects the performance. Although numerous schemes exist on feature weighting for sentiment analysis, efficient and accurate decision of the weights of the features according to their importance is still a big issue. In this paper, a novel approach is proposed that explores the intrinsic information of the feature from the initial corpus for deciding the weights [9]. The main contributions of this paper are summarized as follows. * A novel mathematical model called Category Discriminative Strength (CDS) is proposed, which measures the strength of the terms in differentiating the categories. Also, the Intra-Distribution Measure (IDM) is introduced to characterize the partial significance of the terms inside a category. * A modified Chi-square statistics model is introduced to measure the intra-category dependency of the features. * A fine-grained feature clustering strategy is presented, which properly defines the margin of discriminative features and aggregates the features of similar distributions for efficient feature weighting. * An adaptive weighting strategy is proposed to properly decide the weight of each feature, and the inter-category and intra-category relevance of the features are included to maximize the efficiency of the weighting. An extensive experiment demonstrates that the proposed approach is superior to the existing state-of-the-art schemes in terms of classification accuracy, precision, recall, and F1 measure. The rest of the paper is organized as follows. Section 2 discusses the background of sentiment analysis.