Cloud-Based Core Text Processing Services for Sentiment Analysis

In modern society, Web gradually becomes the portal and window of all kinds of information. People are more likely to express their views on the Internet, mostly would be over the form of text documents. In order to understand users, NLP (Natural Language Processing) methods, such as sentiment analysis, have been gaining popularity. At present, there are some classical methods to solve the text sentiment analysis problem, such as the machine learning method, the classification models NB (Naive Bayes), ME (Maximum Entropy) and SVM (Support Vector Machine). In this paper, we mainly study sentiment analysis for big data scenarios from engineering perspective. This paper proposes core text processing services and discusses the corresponding development details. The contributions are manifolds: Firstly, a new core text processing service Cloud-based Core Text Processing Services (CCTPS) is proposed. Secondly, we propose the use of KNN for regression purposes, resulting in a new algorithm KNNR. Thirdly, this paper formalizes the scenarios of personalized news recommendation and personas portraying in the context of CCTPS. Experimental results of two real-world applications, one for sentiment analysis and the other for personalized news recommendation, to demonstrate the wide practical usability of CCTPS system.