<p>The rejuvenation of applications to harmonize with technological watch is the major challenge for all computer boxes, frameworks and languages are constantly proliferating by offering a range of improvements in terms of security and performance, which pushes all applications to invest in order to align oneself, to orient oneself towards another perspective of application implementation has become a primacy. MVW is considered the new concept of application models where the developer can choose according to his needs, which component, for example, it can be a controller, a directive or a unit test for applications where we use the AngularJS framework, modeling an application is one of the basic steps to reach it , the emergence of new patterns press IT companies to think to renew their application architecture for more security and performance, moving from an old to a new model meets this need. AngularJS is one of the widely used frameworks for modern single-page web application development which is designed to support dynamic views in the applications.</p><p>We propose an UML profile for AngularJS for building a model of an AngularJS web application, and a set of transformations that transform the model into a code template.</p>
Nowadays, sentiment analysis is becoming a very important issue of research. This paper present experimentation on sentiment analysis based on subjective lexicon method. This experimentation is tested over French tweets using "Public Opinion Knowledge (POK)" platform. POK is a platform consists in getting public opinion orientation from text extracted from social network and blogs, which we have developed and presented in previous papers. There are three algorithms as classifiers, which are based on Natural Language Processing Tools. The first is based on OpenNLP, the second on CoreNLP and the third on dependency analysis implemented by CoreNLP. Each classifier consists of three steps, which are Part of Speech Tagging (POS), word polarity classification and sentiment classification algorithm. On the one hand, the results are used to evaluate the use of OpenNLP and CoreNLP, on other, they draw to make a comparison between lexicon and machine-learning approaches. So, experimentation leads us to conclude that tools of sentiment analysis based on lexicon are much performant than those based on machine learning and they can reach a rate of precision of 70% and F-measure of 0.7. Also, we conclude that CoreNLP is more efficient than OpenNLP by 3% of precision, this fact is due to the efficiency of Part of Speech tagging algorithms.
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