A Study on the Effects of Topics on an Opinion Sentences Identification System for Micro-blog Data
Lin Luo · Computer Knowledge and Technology · 2014
As an important stage for information extraction, the problem of Opinion Sentence Identification(OSI) has attracted more and more attentions from NLP researchers in the past decade. Similar to other areas in NLP, most current OSI systems are built based on machine learning(ML) technologies, which often suffer from the problem of domain/topic adaptation. In this paper, an empirical study was conducted to test whether the topic difference among the micro-blog data effects on the performance of an ML-based OSI system, which used rule-based automatic annotation methods to expand the training set. The experimental results indicated that by introducing a topic classifier and performing the training based on the sub topics, the performance of the OSI system for micro-blog data could be improved significantly.