Suggestion Mining from Online Reviews using ULMFiT
Sarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz, Simra, Shahid, Haimin Zhang, Yaman Kumar, Rajiv Ratn Shah, Karan Uppal
TL;DR
This paper presents a suggestion mining system for online reviews using ULMFiT, achieving competitive results in SemEval 2019 by fine-tuning language models and applying preprocessing techniques.
Contribution
The paper introduces a novel application of ULMFiT for suggestion detection in online reviews, with detailed analysis and publicly shared implementation.
Findings
Achieved an F1 score of 0.7011 in SemEval 2019 Task 9
Applied effective preprocessing techniques for improved classification
Ranked 10th out of 34 participants in the challenge
Abstract
In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. Given a sentence, the task asks to predict whether the sentence consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-processing techniques before training the language and the classification model. We further provide detailed analysis of the results obtained using the trained model. Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011. We publicly share our implementation at https://github.com/isarth/SemEval9_MIDAS
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Topic Modeling