With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently.

Automatic classification of text data has been one of important research topics during recent decades. In this research, a new model based on data fusion techniques is introduced which is used for improving text classification effectiveness. This model has two major components, namely feature fusion and decision fusion; therefore, it is called Feature Decision Fusion (FDF) model. In the feature fusion component, two well-known text feature selection algorithms, Chi-Square (X2) and Information Gain (IG) were used; this component applied Ordered Weighted Averaging (OWA) operator in order to make better feature selection. The second component, Decision fusion component, combined two kinds of results using the Majority Voting (MV) algorithm. The results were obtained with feature fusion and without feature fusion. To evaluate the proposed model, K-Nearest Neighbor (KNN), Decision Tree and Perceptron Neural Network algorithms were used for classifying Rueters-21578 dataset documents. Experiments showed that this model can improve effectiveness of text classification in accordance to both Micro-averaged F1 and Macro-averaged F1 measures.

With increasing speed of information and documents on the Web, our need to classify them in different categories and clusters is more necessary. Clustering tries to find related structures in data sets which they are not categorized, yet. Concerning the needs, a new approach for text documents categorization is presented in this paper which includes three phases: pre-processing documents and selection feature, K-Means clustering and Naïve Bayes (NB) optimization. The proposed model uses K-Means and NB algorithms that utilize K-Means algorithm to find minimum distances between features from the center of clusters and NB algorithm for computing the probability of each feature into documents and using them to cluster features, separately. The proposed model optimizes performance of K-Means algorithm by using NB properties in clustering. Therefore, the model overcomes to the challenges of labeling different documents and origin of K-Means algorithm which it refers to categorizing text documents as un-supervised model. Finally, the experiment results of proposed model and K-Means algorithms are evaluated based on evaluation methods and are compared in validated datasets.

Beauties in Hafez's poetry are classified and have degrees. In order to explain the degrees of beauty in Hafez's ghazal, the upcoming research tries to find out the degrees of beauty and the relationships between them, and also, based on classical hermeneutics and "Schleiermacher" style, to interpret the text of Hafez's ghazal technically and grammatically. First, for technical interpretation, it reveals the lexical and idiomatic meanings of the words "Hosn" and "Aan" in the opinions of Muslim thinkers and in specific time-space conditions. Then, for the grammatical interpretation, it explains the semantic features of words similar to beauty, the words associated with and its complementary terms, and on the basis of the frequency of these terms, it starts a quantitative and qualitative interpretation at the same time. Based on the lexical frequency and examples of beauty, it is concluded that "Hosn"(beauty) as a concept that is measured using "Opinion Science", is the first and simplest concept of beauty in Hafez's Ghazal, that is separated in two ways: "God-given beauty" and "Beauty depending on ornaments". "God-given beauty" has the levels of "kindness", "elegance" and "Aan". in fact, "kindness" is higher than "beauty" and it is a quality that has been added to "Hosn". "Elegance" is a quality that can be understood but cannot be described, and finally "Aan" is a mysterious and indescribable quality in the beloved that not everyone can understand and recognize. In the aesthetic system of Hafez's mind, "Aan" is in the center of his attention as "Prototype" and is a criterion for rejecting or accepting other aesthetic attributes.