Abstract
In today’s interconnected and multilingual world, code-mixing of languages on social media is a common occurrence. While many Natural Language Processing (NLP) tasks like sentiment analysis are mature and well designed for monolingual text, techniques to apply these tasks to code-mixed text still warrant exploration. This paper describes our feature engineering approach to sentiment analysis in code-mixed social media text for SemEval-2020 Task 9: SentiMix. We tackle this problem by leveraging a set of hand-engineered lexical, sentiment, and metadata fea- tures to design a classifier that can disambiguate between “positive”, “negative” and “neutral” sentiment. With this model we are able to obtain a weighted F1 score of 0.65 for the “Hinglish” task and 0.63 for the “Spanglish” tasks.- Anthology ID:
- 2020.semeval-1.163
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1227–1232
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.163
- DOI:
- 10.18653/v1/2020.semeval-1.163
- Cite (ACL):
- Laksh Advani, Clement Lu, and Suraj Maharjan. 2020. C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text Using Feature Engineering. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1227–1232, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text Using Feature Engineering (Advani et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.semeval-1.163.pdf
- Data
- SentiMix