.png){kind=icon}
.png){kind=icon}
Return
Aspect Based Multi-Labeling Using SVM Based Ensembler
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3055768.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sentiment analysis is one of the most prominent sub-areas of research in Natural Language Processing (NLP), where it is important to consider implicit or explicit emotions conveyed by review material. Researchers also recognized that the generic feelings derived from the textual material are insufficient, so the sentiment analysis aspect based was coined to extract the emotions from textual data. Multi-labeling based on aspects data can resolve the issue of extracting emotion aspect based. In this work, a novel approach namely: Evolutionary Ensembler (EEn) is proposed to effectively boost the accuracy and diversity of multi-label learners. Unlike traditional multi-label training methods, EEn emphasizes the accuracy and diversity of multi-label-based models. We have used seven datasets (medical, hotel, movies, automobiles, proteins, birds, emotions, news). At first, we applied a pre-processing step to gain the refined and clean data. Second, we have applied the Vader tool with Bag of Words (BoW) for the feature extraction. Third, the word2vec method is applied to draw an association between words. Moreover, the SVM model (tuned with GA) is trained and tested on the refined data. The accuracy of the aspect-based multi-labeling using the SVM-GA on medical, hotel, movies, automobiles, proteins, birds, emotions, news are 93.13%, 94.32%, 94.0%, 95.10%, 90.20%, 93.22%, 90.0%, and 94.0%, respectively. Our proposed model with different dimensions of multi-label datasets shows that EEn is vastly superior to other popular techniques. Experimental outcomes validate the success of the implemented approach among existing benchmark techniques.
Keywords:
Support vector machines
Sentiment analysis
Learning systems
Labeling
Videos
Training
Motion pictures
Sentiment analysis
multi-labeling
aspect based
ML
optimization techniques
SVM
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W