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

Yacouba Conde

doi:10.47191/ijmra/v4-i11-23

Abstract

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 embeddings 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%.

Highlights

In social networking there is the various platform, like Facebook, Twitter, make use of such stages to incredible opinions, as well as any emotional or dramatic emotions on any topic
The expected results show that our proposal is able in n-gram models, and getting to 87% and an FI-score of 85%. These estimated results demonstrated that the knowledge graph uses and opens the opportunity to determine the use of semantics in the sentiment analysis
In terms of deep learning the model combined with semantic texts by knowledge graph and similarity metrics, and expansibility of graph which can be visually inspected ensured results and accuracy(Andreasen et al, 2020)

Summary

INTRODUCTION

In social networking there is the various platform, like Facebook, Twitter, make use of such stages to incredible opinions, as well as any emotional or dramatic emotions on any topic In this situation, intelligent classification models, to use sentiment analysis, have identified efficiency to envisage extra feelings in the texts and to classify the users’ acuity regarding daily life (Tang and Qiu, 2021; Zhang et al, 2020). This knowledge graph is applied to determine the sentiment polarities based on comparison measures between the pre and pro-determine polarities of graphs It is one of the broad application prospects in different areas, including computer networks, graphics, health, sports, and different dialogues interpretation. The expected results show that our proposal is able in n-gram models, and getting to 87% and an FI-score of 85% These estimated results demonstrated that the knowledge graph uses and opens the opportunity to determine the use of semantics in the sentiment analysis. The knowledge graph is not exaggerated by the size of the text or the use of dialogues

LITERATURE

ANALYSIS AND RESULTS

RESULTS

CONCLUSION