Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders

Abstract

We apply the semi-supervised recursive autoencoders (RAE) model for the sentiment classification task of Tibetan short text, and we obtain a better classification effect. The input of the semi-supervised RAE model is the word vector. We crawled a large amount of Tibetan text from the Internet, got Tibetan word vectors by using Word2vec, and verified its validity through simple experiments. The values of parameter α and word vector dimension are important to the model effect. The experiment results indicate that when α is 0.3 and the word vector dimension is 60, the model works best. Our experiment also shows the effectiveness of the semi-supervised RAE model for Tibetan sentiment classification task and suggests the validity of the Tibetan word vectors we trained.

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

“…With the development of internet and social media, people use social sites, websites, blogs, and forums more often as the primary mediums for expressing their comments, views, opinions on services, issues, ideas, and various other things [2]. Thus, sentiment analysis plays an important role in analyzing reviews, views, and opinions presented by users in order to evaluate the feedback on a specific aspect [3]. The results of this analysis are very useful to service providers in improving the quality of their respective services.…”