Neural Sentiment Classification with User and Product Attention

Chen, Huimin; Sun, Maosong; Tu, Cunchao; Lin, Yankai; Liu, Zhiyuan

doi:10.18653/v1/d16-1171

Cited by 283 publications

(173 citation statements)

References 16 publications

(23 reference statements)

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“…LSTM is a specific type of recurrent neural network (RNN) that has proven powerful for modeling long-range dependencies. The LSTM model and its many variants have achieved outstanding performance in sequence-learning problems involving text analysis [22,23]. All of these studies have verified the effectiveness of deep learning in NLP.…”

Section: Deep Learning In Nlpmentioning

confidence: 87%

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

et al. 2017

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Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

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Section: Deep Learning In Nlpmentioning

confidence: 87%

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

et al. 2017

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“…For sentiment analysis, the attention mechanism has been applied to cross-lingual sentiment (Zhou et al, 2016), aspect-level sentiment (Wang et al, 2016) and user-oriented sentiment (Chen et al, 2016). To our knowledge, we are the first to use the attention mechanism to model sentences with respect to targeted sentiments.…”

Section: Related Workmentioning

confidence: 99%

Attention Modeling for Targeted Sentiment

Liu¹,

Zhang²

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

195

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Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for classification. However, they do not explicitly model the contribution of each word in a sentence with respect to targeted sentiment polarities. We investigate an attention model to this end. In particular, a vanilla LSTM model is used to induce an attention value of the whole sentence. The model is further extended to differentiate left and right contexts given a certain target following previous work. Results show that by using attention to model the contribution of each word with respect to the target, our model gives significantly improved results over two standard benchmarks. We report the best accuracy for this task.

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“…Another related research field is documentlevel sentiment classification because we can treat single aspect sentiment classification as an individual document classification task. This line of research includes (Tang et al, 2015b;Chen et al, 2016;Tang et al, 2016; which are based on neural networks in a hierarchical structure. However, they did not work on multiple aspects.…”

Section: Related Workmentioning

confidence: 99%

Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension

Yin¹,

Song²,

Ming³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Document-level multi-aspect sentiment classification is an important task for customer relation management. In this paper, we model the task as a machine comprehension problem where pseudo questionanswer pairs are constructed by a small number of aspect-related keywords and aspect ratings. A hierarchical iterative attention model is introduced to build aspectspecific representations by frequent and repeated interactions between documents and aspect questions. We adopt a hierarchical architecture to represent both word level and sentence level information, and use the attention operations for aspect questions and documents alternatively with the multiple hop mechanism. Experimental results on the TripAdvisor and BeerAdvocate datasets show that our model outperforms classical baselines.

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Neural Sentiment Classification with User and Product Attention

Cited by 283 publications

References 16 publications

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

Attention Modeling for Targeted Sentiment

Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension

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