LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis

TL;DR

This paper presents a Recurrent Convolutional Neural Network approach for sentiment analysis of Hindi-English code-mixed social media text, achieving competitive results in SemEval-2020.

Contribution

It introduces a novel R-CNN model combining recurrent and convolutional layers specifically for code-mixed sentiment analysis.

Findings

Achieved an F1 score of 0.69 on test data.

Secured 9th place in the SentiMix Hindi-English subtask.

Demonstrated effectiveness of R-CNN for code-mixed sentiment tasks.

Abstract

This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix Hindi-English subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent Convolutional Neural Network that combines both the recurrent neural network and the convolutional network to better capture the semantics of the text, for code-mixed sentiment analysis. The proposed system obtained 0.69 (best run) in terms of F1 score on the given test data and achieved the 9th place (Codalab username: somban) in the SentiMix Hindi-English subtask.