A Hybrid Deep Learning Architecture for Sentiment Analysis
Md Shad Akhtar, Ayush Kumar, Asif Ekbal, Pushpak Bhattacharyya
Abstract
In this paper, we propose a novel hybrid deep learning archtecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification. We evaluate our proposed approach for coarse-grained (i.e. sentence level) as well as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets covering varying domains. In order to show that our proposed method is generic in nature we also evaluate it on two benchmark English datasets. Evaluation shows that the results of the proposed method are consistent across all the datasets and often outperforms the state-of-art systems. To the best of our knowledge, this is the very first attempt where such a deep learning model is used for less-resourced languages such as Hindi.- Anthology ID:
- C16-1047
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 482–493
- Language:
- URL:
- https://aclanthology.org/C16-1047
- DOI:
- Cite (ACL):
- Md Shad Akhtar, Ayush Kumar, Asif Ekbal, and Pushpak Bhattacharyya. 2016. A Hybrid Deep Learning Architecture for Sentiment Analysis. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 482–493, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Hybrid Deep Learning Architecture for Sentiment Analysis (Akhtar et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/C16-1047.pdf