Conference proceeding
Comparative Study of CNN and LSTM based Attention Neural Networks for Aspect-Level Opinion Mining
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp 2141-2150
01 Jan 2018
Abstract
Aspect-level opinion mining aims to find and aggregate opinions on opinion targets. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. In this paper, we propose and compare two interactive attention neural networks for aspect-level opinion mining, one employs two bi-directional Long-Short-Term-Memory (BLSTM) and the other employs two Convolutional Neural Networks (CNN). Both frameworks learn opinion targets and the context respectively, followed by an attention mechanism that integrates hidden states learned from both the targets and context. We compare our model with state-of-the-art baselines on two SemEval 2014 datasets(1). Experiment results show that our models obtain competitive performances against the baselines on both datasets. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results. The quantitative and qualitative comparisons in our work aim to give basic guidance for neural network selection in similar tasks.
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Details
- Title
- Comparative Study of CNN and LSTM based Attention Neural Networks for Aspect-Level Opinion Mining
- Creators
- Wei Quan - Drexel UniversityZheng Chen - Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USAJianliang Gao - Cent South Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R ChinaXiaohua Tony Hu - Drexel University, Information Science
- Publication Details
- 2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp 2141-2150
- Series
- IEEE International Conference on Big Data
- Publisher
- IEEE
- Number of pages
- 10
- Grant note
- III 1815256; III 1744661; CNS 1650431 / NSF; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000468499302030
- Scopus ID
- 2-s2.0-85062619998
- Other Identifier
- 991019170565104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods