Text Sentiment Analysis Algorithm Optimization and Platform Development in Social Network

Abstract

The sentiment information implied in the content of Social Networking Services (SNS) is of great technical and social significance, which has attracted a lot of researchers in different fields. Chinese text sentiment analysis is still in its starting stage, and most of the previous sentiment analysis algorithms are either inaccurate or inefficient. The purpose of this paper is to propose an algorithm performing well in both accuracy and efficiency, and apply it into a real time platform. A sentiment analysis algorithm on Chinese micro-blog content is introduced firstly, which achieves an outstanding accuracy but performs badly in efficiency. Then we optimize it with three strategies: data structure optimization, query strategy optimization, and parallel optimization. The experiment shows these strategies are very effective and the optimized algorithm is over 100 times more efficient than the basic algorithm. Based on the optimized algorithm, a text sentiment analysis platform is developed for real time sentiment. The platform offers two main functions including text sentiment analysis and user sentiment timeline. The results are outputted through a group of open APIs, which can be revoked by other developers and reduces their development costs. As a whole, the platform performs well on responding rate, capacity of concurrent users, stability and expandability.