Yapay sinir ağının benzer analizlerin sınıflandırılmasında farklı tekniklerin karşılaştırmalı analiziyle kullanılması

Sentiment Analysis means identifying the favorable, negative or neutral opinion or reviewer opinions expressed in a piece of job. In social media surveillance sentiment assessment is helpful to automatically characterize the general impression or mood of the customers as replicated on their social media for a particular brand or business and determine if they are regarded favorably or negatively on the Internet. This article examines the machine-based learning approaches to feeling assessment and highlights the key characteristics of methods. Prominently used techniques and methods are Naïve Bayes, Maximum Entropy and Support Vector Machine, the most near-neighbor classification of Machine Learning-based feeling assessment. Naïve Bayes ' depiction is quite easy but does not give rise to wealthy assumptions. The hypothesis that characteristics are independent is too restrictive. Maximum Entropy estimates the distribution of probability by information, but it does well only with dependent characteristics. For SVM the kernel is correct, but the way to deal with multi-class issues is not standardized. A method which combines neural networks and fuzzy logic often is used to improve the efficiency of correlations and dependencies between variables.