Abstract

The online learning, is one that continuously adapts to arriving data, and gets updated incrementally instance by instance. In this paper, we compare the performance of different online machine learning algorithms for the task of sentiment analysis on challenging text datasets. We assess the models using a wide range of metrics, such as microF1, macroF1, accuracy, and running time. Our experiments have revealed that these online models provide a viable alternative to traditional offline machine learning in sentiment analysis, in fraction of the time.

This work is licensed under a Creative Commons Attribution 4.0 License.