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.

翻译：长期以来收集了大量反馈数据，现有许多反馈分析模型主要针对英语语言开发。对于缺乏自然语言处理语料库和工具（如词汇语料库、句法结构规则等）的语言而言，识别反馈的概念既具挑战性又至关重要。本文研究使用两种不同词向量嵌入方法进行蒙古语深度学习反馈分类，并比较了所提出方法的结果。我们使用了2012-2018年间收集的西里尔蒙古语反馈数据。结果表明，使用自有数据集训练的词向量嵌入使基于深度学习的分类模型在两个分类任务中分别达到80.1%和82.7%的最佳准确率。