Joint and Pipeline Probabilistic Models for Fine-Grained Sentiment Analysis: Extracting Aspects, Subjective Phrases and their Relations

Sentiment analysis and opinion mining are often addressed as a text classification or entity recognition problem, involving the detection or classification of aspects and subjective phrases. Many approaches do not model the relation between aspects and subjective phrases explicitly, implicitly assuming that a subjective phrase refers to a certain aspect if they co-occur together in the same sentence, thus potentially sacrificing accuracy. Instead, in the approach presented in this paper, we model the relation between aspects and subjective phrases explicitly, exploiting a flexible model based on imperatively defined factor graphs (IDF). The extraction of subjective phrases, aspects and the relation between them is modeled as a joint inference problem and compared to a pure pipeline architecture. Our goal is to analyse and quantify to what extent a joint model outperforms a pipeline model in terms of extraction of aspects, subjective phrases and the relation between them. Our results show that, while we have a substantial improvement on predicting targets using a joint inference model, the performance on subjective phrase detection and relation extraction actually decreases only slightly.