Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network

Abstract

We investigate the task of open domain opinion relation extraction. Given a large number of unlabelled texts, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is designed to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset, and show that the proposed model is able to achieve promising performances without any human annotations.

Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons

“…This assumption, however, might be strong for fine-granularity analyses of text sentiments: words (especially, neural words such as "long", "fast") could exhibit different orientations even in the same domain (Figure 1). To collect more detailed information of a sentiment, another branch of works (aspect-based sentiment analysis (Pontiki et al, 2014;Zhou et al, 2020a,b), opinion relation extraction (Sun et al, 2017)) attempt find answers of "who express what opinion on which target" for opinion bearing texts. Existing solutions heavily rely on manual annotations and linguistic rules, which are either hard to scale-up or hard to be complete.…”