De-Mixing Sentiment from Code-Mixed Text
Yash Kumar Lal, Vaibhav Kumar, Mrinal Kanti Dhar, Manish Shrivastava, Philipp Koehn · 2019
Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence.It is an increasingly common occurrence in today's multilingual society and poses a big challenge when encountered in different downstream tasks.In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data.Our method consists of three components, each seeking to alleviate different issues.We first generate subword level representations for the sentences using a CNN architecture.The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs -the Collective and Specific Encoder.The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words.This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results -83.54% accuracy and 0.827 F1 score -on a benchmark dataset.