Extracting Aspect Specific Opinion Expressions

Abstract

Opinionated expression extraction is a central problem in fine-grained sentiment analysis. Most existing works focus on either generic subjective expression or aspect expression extraction. However, in opinion mining, it is often desirable to mine the aspect specific opinion expressions (or aspectsentiment phrases) containing both the aspect and the opinion. This paper proposes a hybrid generative-discriminative framework for extracting such expressions. The hybrid model consists of (i) an unsupervised generative component for modeling the semantic coherence of terms (words/phrases) based on their collocations across different documents, and (ii) a supervised discriminative sequence modeling component for opinion phrase extraction. Experimental results using Amazon.com reviews demonstrate the effectiveness of the approach that significantly outperforms several state-of-the-art baselines.

DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction

“…For supervised methods, the ATE task is usually treated as a sequence labeling problem solved by CRF. For the ASC task, a large body of literature has tried to utilize the relation or position between the aspect terms and the surrounding context words as the relevant information or context for prediction (Tang et al, 2016a;Laddha and Mukherjee, 2016). Convolution neural networks (CNNs) (Poria et al, 2016;Li and Xue, 2018), attention network (Wang et al, 2016b;Ma et al, 2017;He et al, 2017), and memory network are also active approaches.…”

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

“…For the ATE task, unsupervised methods such as frequent pattern mining (Hu and Liu, 2004), rule-based approach (Qiu et al, 2011;Liu et al, 2015), topic modeling (He et al, 2011;Chen et al, 2014), and supervised methods such as sequence labeling based models (Wang et al, 2016a;Yin et al, 2016;Xu et al, 2018;Luo et al, 2019a;Ma et al, 2019) are two main directions. For the ASC task, the relation or position between the aspect terms and the surrounding context words are usually used (Tang et al, 2016;Laddha and Mukherjee, 2016).…”

Review-Driven Answer Generation for Product-Related Questions in E-Commerce

“…Many works focus on aspect-based extraction and opinion mining from user reviews [14,15,19,30]. The techniques utilized to address this task include neural networks [15,30], topic modeling [9,19,29] and rule-based mining [14]. However, these existing works mainly present the aspect specific information with a bag-of-words representation, such as opinion terms or sentiment polarity.…”

“…Many aspect-based opinion mining techniques have been developed to mitigate this information overload [9,29,30]. However, the relevant information is mainly expressed in a bag-of-words (BOW) fashion by the existing solutions.…”