SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data

Rajalakshmi S, Angel Suseelan, S Milton Rajendram, Mirnalinee T T


Abstract
This paper describes the work on mining the suggestions from online reviews and forums. Opinion mining detects whether the comments are positive, negative or neutral, while suggestion mining explores the review content for the possible tips or advice. The system developed by SSN-SPARKS team in SemEval-2019 for task 9 (suggestion mining) uses a rule-based approach for feature selection, SMOTE technique for data augmentation and deep learning technique (Convolutional Neural Network) for classification. We have compared the results with Random Forest classifier (RF) and MultiLayer Perceptron (MLP) model. Results show that the CNN model performs better than other models for both the subtasks.
Anthology ID:
S19-2217
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1237–1241
Language:
URL:
https://aclanthology.org/S19-2217
DOI:
10.18653/v1/S19-2217
Bibkey:
Cite (ACL):
Rajalakshmi S, Angel Suseelan, S Milton Rajendram, and Mirnalinee T T. 2019. SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1237–1241, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data (S et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2217.pdf