Abstract
Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space. However, the importance of capturing implicit global structural information that resides in the input space is largely unexplored. In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model. Our experimental results show that properly capturing both information is able to lead to better performance than competitive existing approaches. We also conduct extensive experiments to investigate our model’s effectiveness and robustness.
Highlights
Targeted sentiment analysis (TSA) is an important task useful for public opinion mining (Pang and Lee, 2008; Liu, 2010; Ortigosa et al, 2014; Smailovicet al., 2013; Li and Wu, 2010)
Note that EI- which models flexible explicit structures and less implicit structural information, achieves better performance than most of the baselines, indicating flexible explicit structures contribute a lot to the performance boost
The Pipeline model as well as Joint and Collapse models in their work capture fixed explicit structures. Such two models rely on multilayer perceptron (MLP) to obtain the local context features for implicit structures
Summary
Targeted sentiment analysis (TSA) is an important task useful for public opinion mining (Pang and Lee, 2008; Liu, 2010; Ortigosa et al, 2014; Smailovicet al., 2013; Li and Wu, 2010). The task focuses on predicting the sentiment information towards a specific target phrase, which is usually a named entity, in a given input sentence. TSA in the literature may refer to either of the two possible tasks under two different setups: 1) predicting the sentiment polarity for a given specific target phrase (Dong et al, 2014; Wang et al, 2016; Zhang et al, 2016; Xue and Li, 2018); 2) jointly predicting the targets together with the sentiment polarity assigned to each target (Mitchell et al, 2013; Zhang et al, 2015; Li and Lu, 2017; Ma et al, 2018). Each target is associated with a sentiment, where we use + for denoting positive polarity, 0 for neutral and − for negative
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have