Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis

Chen, Tao; Xu, Ruifeng; He, Yulan; Xia, Yunqing; Wang, Xuan

doi:10.1109/mci.2016.2572539

Cited by 106 publications

(47 citation statements)

References 38 publications

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“…As the emergence of distributed representation learning, user embeddings obtained by neural networks are widely used. [6] employs RNN-GRU to learn user embeddings from the temporal ordered review documents. [30] learns user embedding vectors from word embedding vectors and applies them to recommending scholarly microblogs.…”

Section: Related Workmentioning

confidence: 99%

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

Liu

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

255

190

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Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items. The matching stage retrieves candidate items relevant to user interests, while the ranking stage sorts candidate items by user interests. Thus, the most critical ability is to model and represent user interests for either stage. Most of the existing deep learning-based models represent one user as a single vector which is insufficient to capture the varying nature of user's interests. In this paper, we approach this problem from a different view, to represent one user with multiple vectors encoding the different aspects of the user's interests. We propose the Multi-Interest Network with Dynamic routing (MIND) for dealing with user's diverse interests in the matching stage. Specifically, we design a multi-interest extractor layer based on capsule routing mechanism, which is applicable for clustering historical behaviors and extracting diverse interests. Furthermore, we develop a technique named label-aware attention to help learn a user representation with multiple vectors. Through extensive experiments on several public benchmarks and one largescale industrial dataset from Tmall, we demonstrate that MIND can achieve superior performance than state-of-the-art methods for recommendation. Currently, MIND has been deployed for handling major online traffic at the homepage on Mobile Tmall App.

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Section: Related Workmentioning

confidence: 99%

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

Liu

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

255

190

View full text Add to dashboard Cite

show abstract

“…Individualities are mostly considered in sentiment analysis when analyzing product-review texts (Gong et al, 2016;Chen et al, 2016b;Wu et al, 2018). A common issue that challenges the research of this area is data sparsity.…”

Section: Related Workmentioning

confidence: 99%

A Personalized Sentiment Model with Textual and Contextual Information

Guo¹,

Höhn²,

Schommer³

2019

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective. Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a userspecific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.

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“…Then, they employed a RNN with gated recurrent units to learn distributed representations of users and products. Finally, they learnt a sentiment classifier from user, product and review representations [32].…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning in Sentiment Classification with Deep Neural Networks

Pagliarani

Moro

Pasolini

et al. 2019

Communications in Computer and Information Science

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Cross-domain sentiment classifiers aim to predict the polarity (i.e. sentiment orientation) of target text documents, by reusing a knowledge model learnt from a different source domain. Distinct domains are typically heterogeneous in language, so that transfer learning techniques are advisable to support knowledge transfer from source to target. Deep neural networks have recently reached the state-of-the-art in many NLP tasks, including in-domain sentiment classification, but few of them involve transfer learning and cross-domain sentiment solutions. This paper moves forward the investigation started in a previous work [1], where an unsupervised deep approach for text mining, called Paragraph Vector (PV), achieved cross-domain accuracy equivalent to a method based on Markov Chain (MC), developed ad hoc for crossdomain sentiment classification. In this work, Gated Recurrent Unit (GRU) is included into the previous investigation, showing that memory units are beneficial for cross-domain when enough training data are available. Moreover, the knowledge models learnt from the source domain are tuned on small samples of target instances to foster transfer learning. PV is almost unaffected by fine-tuning, because it is already able to capture word semantics without supervision. On the other hand, fine-tuning boosts the crossdomain performance of GRU. The smaller is the training set used, the greater is the improvement of accuracy.

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Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis

Cited by 106 publications

References 38 publications

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

A Personalized Sentiment Model with Textual and Contextual Information

Transfer Learning in Sentiment Classification with Deep Neural Networks

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