経済学および管理科学の国際ジャーナル

Solicitation of Knowledge Graph Enhanced Neural Network Objects Detection by Sentiment Analysis

Abstract

Yacouba Conde and Zhoulianying

In the machine learning technique, the knowledge graph is advancing swiftly; however, the basic models are not able to grasp all the affluence of the script that comes from the different personal web graphics, social media, ads, and diaries, etc., ignoring the semantic of the basic text identification. The knowledge graph provides a real way to extract structured knowledge from the texts and desire images of neural network, to expedite their semantics examination. In this study, we propose a new hybrid analytic approach for sentiment evaluation based on knowledge graphs, to identify the polarity of sentiment with positive and negative attitudes in short documents, particularly in 4 chirps. We used the tweets graphs, then the similarity of graph highlighted metrics and algorithm classification pertain sentimentality pre-dictions. This technique facilitates the explicability and clarifies the results in the knowledge graph. Also, we compare our differentiate the embedding’s n-gram based on sentiment analysis and the result is indicated that our study can outperform classical n-gram models, with an F1-score of 89% and recall up to 90%.