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SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints

Authors:

Xin Liu,

Wei Pan,

Hailong LiuAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 207 - 210

https://doi.org/10.1145/3184558.3186980

Published: 23 April 2018 Publication History

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Abstract

The state-of-the-art techniques for aspect-level sentiment analysis focus on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their practical performance may fall short of expectations due to semantic complexity of natural languages. Motivated by the observation that linguistic hints (e.g. explicit sentiment words and shift words) can be strong indicators of sentiment, we present a joint framework, SenHint, which integrates the output of deep neural networks and the implication of linguistic hints into a coherent reasoning model based on Markov Logic Network (MLN). In SenHint, linguistic hints are used in two ways: (1) to identify easy instances, whose sentiment can be automatically determined by machine with high accuracy; (2) to capture implicit relations between aspect polarities. We also empirically evaluate the performance of SenHint on both English and Chinese benchmark datasets. Our experimental results show that SenHint can effectively improve accuracy compared with the state-of-the-art alternatives.

References

[1]

Yang Chen and Daisy Zhe Wang. 2014. Knowledge expansion over probabilistic knowledge bases Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, 649--660.

Digital Library

Google Scholar

[2]

Pedro Domingos and Daniel Lowd. 2009. Markov logic: An interface layer for artificial intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 3, 1 (2009), 1--155.

Digital Library

Google Scholar

[3]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.

Digital Library

Google Scholar

[4]

Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, and Eric Xing. 2016. Harnessing deep neural networks with logic rules. arXiv preprint arXiv:1603.06318 (2016).

Google Scholar

[5]

Clayton J Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text Eighth international AAAI conference on weblogs and social media.

Google Scholar

[6]

Tony Mullen and Nigel Collier. 2004. Sentiment Analysis using Support Vector Machines with Diverse Information Sources. EMNLP, Vol. Vol. 4. 412--418.

Google Scholar

[7]

Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, and Orphee De Clercq. 2016. SemEval-2016 Task 5: Aspect Based Sentiment Analysis International Workshop on Semantic Evaluation. 19--30.

Google Scholar

[8]

Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, and Christopher Ré. 2015. Incremental knowledge base construction using deepdive. Proceedings of the VLDB Endowment Vol. 8, 11 (2015), 1310--1321.

Digital Library

Google Scholar

[9]

Yanyan Wang, Qun Chen, Xin Liu, Murtadha Ahmed, Zhanhuai Li, Wei Pan, and Hailong Liu. 2018. A Joint Framework for Aspect-level Sentiment Analysis by Deep Neutral Networks and Linguistic Hints(technical report). Technical Report. http://www.wowbigdata.com.cn/SenHint/senhint-report.pdf

Google Scholar

[10]

Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016. Attention-based LSTM for Aspect-level Sentiment Classification Conference on Empirical Methods in Natural Language Processing. 606--615.

Google Scholar

[11]

Ce Zhang. 2013. Towards high-throughput gibbs sampling at scale:a study across storage managers ACM SIGMOD International Conference on Management of Data. 397--408.

Digital Library

Google Scholar

Cited By

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Wang YChen QAhmed MLi ZPan WLiu H(2019)Joint Inference for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic HintsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2947587(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2947587

Index Terms

SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Information & Contributors

Information

Published In

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Demonstration

Funding Sources

Ministry of Science and Technology of China National Key Research and Development Program
National Natural Science Foundation of China

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WWW '18

Sponsor:

IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Wang YChen QAhmed MLi ZPan WLiu H(2019)Joint Inference for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic HintsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2947587(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2947587

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