Neural Sentiment Classification with User and Product Attention
By Huimin Chen, Maosong Sun, Cunchao Tu, Yankai Lin, Zhiyuan Liu. 2016
315 citations recorded by the source corpus.
Document-level sentiment classification aims to predict user’s overall sentiment in a document about a product. However, most of existing methods only focus on local text information and ignore the global user preference and product characteristics. Even though some works take such information into account, they usually suffer from high model complexity and only consider word-level preference rather than semantic levels. To address this issue, we propose a hierarchical neural network to incorporate global user and product information into sentiment clas-sification. Our model first builds a hierarchical LSTM model to generate sentence and document representations. Afterwards, user and product information is considered via at-tentions over different semantic levels due to its ability of capturing crucial semantic components. The experimental results show that our model achieves significant and consistent improvements compared to all state-of-the-art methods. The source code of this paper can be obtained from https://github. com/thunlp/NSC .
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
Citations in the corpus
- Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN (2018, background): “We used the implementation release along [7].”
- Deep Communicating Agents for Abstractive Summarization (2018, background): “…Lu-ong et al. (2015) proposes global and local attention networks for machine translation, while others investigate hierarchical attention networks for document classification (Yang et al., 2016), sentiment classification (Chen et al., 2016), and dialog response selection (Zhou et al., 2016).”
- From Eliza to XiaoIce: challenges and opportunities with social chatbots (2018, background): “It also detects the sentiment reflected in the input message and infers the user’s emotion status (Tokuhisa et al., 2008; Mower et al., 2011; Socher et al., 2013; Yang et al., 2016; Chen et al., 2016).”
- Deep learning for sentiment analysis: A survey (2018, background): “Chen et al. 100 proposed a Content-based Social Influence Model (CIM) to make opinion behaviour predictions of twitter users.”
- A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “[144] designed a hierarchical LSTM with attention mechanism to generate sentence and document representations, which incorporates global user and product information to prioritize the most contributing items.”
- Semantic Text Matching for Long-Form Documents (2019, methodology): “Besides machine translation, the attention mechanism benefits many applications, such as caption generation [51], document classification [10, 52], and question answering [40].”
- Sentiment Analysis of Comment Texts Based on BiLSTM (2019, background): “[32] integrated user and product information into a hierarchical neural network to implement sentiment analysis of user product reviews.”
- Sentiment Classification by Leveraging the Shared Knowledge from a Sequence of Domains (2019, background): “Many neural network based sentiment classification techniques have been reported [4,27,32,41,48].”
- dEFEND: Explainable Fake News Detection (2019, methodology): “Recently, researchers find that hierarchical attention neural networks [50] are very practical and useful to learn document representations [4] with highlighting important words or sentences for classification.”
- Improving the Interpretability of Neural Sentiment Classifiers via Data Augmentation (2019, methodology): “Sentiment analysis is one of the most widely-used applications of language technology, impacting both industry (Howard and Ruder, 2018; Li et al., 2018; Chen et al., 2016) and other NLP research topics (Feldman, 2013; Pang et al., 2002; Liu, 2012).”
- An Unsupervised Neural Attention Model for Aspect Extraction (2017, background): “, 2015), sentiment classification (Chen et al., 2016; Tang et al., 2016), and question answering (Hermann et al.”