Offline vs. Online Sentiment Analysis: Issues With Sentiment Analysis of Online Micro-Texts

摘要

Recently, the social networking sites (SNSs) have proven their immense power of prediction for predicting the results of the real-world events. However, for real-time monitoring of the world activities via microblogging site like Twitter, it is important to perform the sentiment analysis of online micro-texts in real-time to support fast and intelligent decision-making and hence to execute the appropriate actions in the real world in real-time. In this context, this paper discusses the online sentiment analysis process of online micro-texts in perspectives of the real-time analysis process. In addition, this paper argues the non-applicability of the classical time consuming Natural Language Processing (NLP) methods and the affinity of Machine Learning (ML) methods in performing the online sentiment analysis by contrasting it with offline sentiment analysis. Furthermore, it also formalized the online sentiment analysis process of online micro-texts by raising novel issues and proposing new performance measures for online sentiment analysis.