Currently, the increasing number of available Web Services (WS) over the Internet has induced the urgency for proposing new ways for searching and categorizing such software pieces. Normally, WS functionality is detailed through the WSDL description language, resulting in a structured document that includes a great variety of features definition. One of the WSDL inner features "documentation" is designed to describe the Web Service functionality, in natural language, which could help to classify and find WS. Nevertheless, the majority of WS lack of that description. To tackle this problem, this paper presents an analysis of the WSDL inner feature information that can assist to classify WS, without any extra data. The experiments carried out on three different WSDL collections showed that only with minimal information is possible to increase the performance of automatic WS classification.

In this work, a model for textual emotion classification based on Ranking technique is presented. The Ranking technique uses the frequencies of words in order to assign a relevance for each in a tweets (Spanish) after calculating the total relevance of the tweet for each classes. The classes are associated to four emotions: happiness, sadness, anger and fear and the highest relevance indicates to which class the tweet belongs. The training and test corpora are created by manually selected key words as references for a crawling tool, both contain manually tagged tweets extracted from Twitter; the training corpus was validated by K-Fold Cross Validation having a 90% of acceptance. The results are compared with Naïve Bayes and Bigrams Probabilities models using precision, recall and F-measure.