JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags
This addresses sentiment analysis for social media users in Tamil-speaking regions, but it is incremental as it applies existing methods to a specific code-mixed dataset.
The paper tackled sentiment classification of code-mixed Tamil tweets from social media, achieving precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively using bi-directional LSTMs with language tagging.
Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts. Since the social media texts are not in one language and are largely code-mixed in nature, the traditional sentiment classification models fail to produce acceptable results. This paper tries to solve this very research problem and uses bi-directional LSTMs along with language tagging, to facilitate sentiment tagging of code-mixed Tamil texts that have been extracted from social media. The presented algorithm, when evaluated on the test data, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.