In multilingual societies like India, code-mixed social media texts comprise
the majority of the Internet. Detecting the sentiment of the code-mixed user
opinions plays a crucial role in understanding social, economic and political
trends. In this paper, we propose an ensemble of character-trigrams based LSTM
model and word-ngrams based Multinomial Naive Bayes (MNB) model to identify the
sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model
combines the strengths of rich sequential patterns from the LSTM model and
polarity of keywords from the probabilistic ngram model to identify sentiments
in sparse and inconsistent code-mixed data. Experiments on reallife user
code-mixed data reveals that our approach yields state-of-the-art results as
compared to several baselines and other deep learning based proposed methods.

本研究提出了基于字符三元组 LSTM 模型和基于词元素的多项式朴素贝叶斯 (MNB) 模型的集成模型，用于识别印地语 - 英语 (Hi-En) 混合数据的情感极性，实验结果表明，相较于几个基准和其他基于深度学习的提出的方法，我们的方法在真实用户混合数据上取得了最先进的结果。