Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification
- Authors
- Khan, Jawad; Ahmad, Niaz; Alam, Aftab; Lee, Youngmoon
- Issue Date
- Oct-2022
- Publisher
- Association for Computational Linguistics (ACL)
- Citation
- Proceedings of the 2022 COLING Workshop: The 8th Workshop on Noisy User-generated Text (W-NUT 2022), pp 101 - 105
- Pages
- 5
- Indexed
- OTHER
- Journal Title
- Proceedings of the 2022 COLING Workshop: The 8th Workshop on Noisy User-generated Text (W-NUT 2022)
- Start Page
- 101
- End Page
- 105
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114491
- Abstract
- Sentiment analysis is essential to process and understand unstructured user-generated content for better data analytics and decision-making. State-of-the-art techniques suffer from a high dimensional feature space because of noisy and irrelevant features from the noisy user-generated text. Our goal is to mitigate such problems using DNN-based text classification and popular word embeddings (Glove, fastText, and BERT) in conjunction with statistical filter feature selection (mRMR and PCA) to select relevant sentiment features and pick out unessential/irrelevant ones. We propose an effective way of integrating the traditional feature construction methods with the DNN-based methods to improve the performance of sentiment classification. We evaluate our model on three real-world benchmark datasets demonstrating that our proposed method improves the classification performance of several existing methods.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF ROBOT ENGINEERING > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114491)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.