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Translating Sentimental Statements Using Deep Learning Techniques

Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
Author to whom correspondence should be addressed.
Electronics 2021, 10(2), 138;
Received: 5 December 2020 / Revised: 6 January 2021 / Accepted: 7 January 2021 / Published: 10 January 2021
(This article belongs to the Special Issue Applications of Machine Learning in Big Data)
Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed. View Full-Text
Keywords: deep learning; natural language processing; text mining; semantics deep learning; natural language processing; text mining; semantics
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MDPI and ACS Style

Huang, Y.-F.; Li, Y.-H. Translating Sentimental Statements Using Deep Learning Techniques. Electronics 2021, 10, 138.

AMA Style

Huang Y-F, Li Y-H. Translating Sentimental Statements Using Deep Learning Techniques. Electronics. 2021; 10(2):138.

Chicago/Turabian Style

Huang, Yin-Fu, and Yi-Hao Li. 2021. "Translating Sentimental Statements Using Deep Learning Techniques" Electronics 10, no. 2: 138.

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