Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

被引：0
作者：
J. Maruthupandi [1 ]
S. Sivakumar [2 ]
V. Senthil Kumar [2 ]
P. Balaji Srikaanth [4 ]
机构：
[1] Department of Computer Science and Engineering, New Horizon College of Engineering, Karnataka, Bangalore
[2] Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tamilnadu, Trichy
[3] Department of Networks and Communication, SRM Institute of Science and Technology, Tamilnadu, Chennai
[4] Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai
来源：
SN Computer Science | / 6卷 / 2期
关键词：
Classification; Degree of correlation network model (DCNM); Heap based optimization (HbO); Opinion mining; Self-attention based deep analyzing network (SA-DAN); Semantic analysis; Social data;
D O I：
10.1007/s42979-024-03628-0
中图分类号：
学科分类号：
摘要：
Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
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