KONTROL EKSPRESI WAJAH BERDASARKAN KLASIFIKASI TEKS MENGGUNAKAN METODE NAIVE BAYES

Communication can be done from verbal and non verbal information like writing which is derived from word, sentence, paragraph etc. to find texts information using texts classification.In that classification process will use set data which has been known its emotional class such as: anger, joy and sadness by using Naïve Bayes method. It will be seen how far that method can classify emotional data in English. The result from text classification is used to control face expression by VRML program to visualize face expression. Key word: texts classification, emotion, naïve bayes, vrml

Content-Based Book Recommending Using Learning for Text Categorization

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use social filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.Comment: 8 pages, 3 figures, Submission to Fourth ACM Conference on Digital Librarie

Sampling with Confidence: Using k-NN Confidence Measures in Active Learning

Active learning is a process through which classifiers can be built from collections of unlabelled examples through the cooperation of a human oracle who can label a small number of examples selected as most informative. Typically the most informative examples are selected through uncertainty sampling based on classification scores. However, previous work has shown that, contrary to expectations, there is not a direct relationship between classification scores and classification confidence. Fortunately, there exists a collection of particularly effective techniques for building measures of classification confidence from the similarity information generated by k-NN classifiers. This paper investigates using these confidence measures in a new active learning sampling selection strategy, and shows how the performance of this strategy is better than one based on uncertainty sampling using classification scores

Pruning the vocabulary for better context recognition

Language independent `bag-of-words' representations are surprisingly effective for text classification. The representation is high dimensional though, containing many nonconsistent words for text categorization. These non-consistent words result in reduced generalization performance of subsequent classifiers, e.g., from ill-posed principal component transformations. In this communication our aim is to study the effect of reducing the least relevant words from the bagof -words representation. We consider a new approach, using neural network based sensitivity maps and information gain for determination of term relevancy, when pruning the vocabularies. With reduced vocabularies documents are classified using a latent semantic indexing representation and a probabilistic neural network classifier. Reducing the bag-of-words vocabularies with 90%-98%, we find consistent classification improvement using two mid size data-sets. We also study the applicability of information gain and sensitivity maps for automated keyword generation

Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization

he finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments. With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation, so that the analytically tractable solution of the IBLMM can be derived in an effective way. The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization