Translating sentimental statements using deep learning techniques

Abstract

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.