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.- Anthology ID:
- 2022.wnut-1.11
- Volume:
- Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 101–105
- Language:
- URL:
- https://aclanthology.org/2022.wnut-1.11
- DOI:
- Cite (ACL):
- Jawad Khan, Niaz Ahmad, Aftab Alam, and Youngmoon Lee. 2022. Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 101–105, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification (Khan et al., WNUT 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.wnut-1.11.pdf
- Data
- SST, SST-2