Optimizing Language Models for Polarity Classification

摘要

This paper investigates the usage of various types of language models on polarity text classification - a subtask in opinion mining which deals with distinguishing between positive and negative opinions in natural language. We focus on the intrinsic benefit of different types of language models. This means that we try to find the optimal settings of a language model by examining different types of normalization, their interaction with smoothing and the benefit of class-based modeling.