Name
Title | ||
|---|---|---|
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering |
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 features 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 |
Year | Venue | DocType |
|---|---|---|
2020 | SemEval@COLING | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laksh Advani | 1 | 0 | 0.34 |
| Clement Lu | 2 | 0 | 0.34 |
| Suraj Maharjan | 3 | 1 | 1.12 |