Deep learning model for Mongolian Citizens Feedback Analysis using Word Vector Embeddings
This addresses feedback analysis for Mongolian speakers, but it is incremental as it applies existing methods to a new language.
The paper tackled feedback classification in Mongolian, a language lacking NLP resources, by using two word embeddings with deep learning, achieving best accuracies of 80.1% and 82.7% on Cyrillic feedback data from 2012-2018.
A large amount of feedback was collected over the years. Many feedback analysis models have been developed focusing on the English language. Recognizing the concept of feedback is challenging and crucial in languages which do not have applicable corpus and tools employed in Natural Language Processing (i.e., vocabulary corpus, sentence structure rules, etc). However, in this paper, we study a feedback classification in Mongolian language using two different word embeddings for deep learning. We compare the results of proposed approaches. We use feedback data in Cyrillic collected from 2012-2018. The result indicates that word embeddings using their own dataset improve the deep learning based proposed model with the best accuracy of 80.1% and 82.7% for two classification tasks.