Abstract
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.
Highlights
Sentiment analysis is an active research field that is being applied to different applications, i.e., understanding consumer’s feedbacks and public perceptions, and tracking real-world events [1]
The relation among words needs to be precisely defined and the word2vec framework offers three million words mapped with the same amount of phrases and words, it is quite possible to have a suitable relationship among words from such a large repository
A novel assembler based on an optimization algorithm has been proposed to tackle this issue
Summary
Sentiment analysis is an active research field that is being applied to different applications, i.e., understanding consumer’s feedbacks and public perceptions, and tracking real-world events [1]. It is well familiar that by building multi-base learners in the single-label context, ensemble-learning enhances generalization capacity of the learning system and decreases the risk of over-fitting [12]. In the case of ML learning, the generalization potential of the ML learning system can be greatly enhanced, if we put together a cluster of simple multi-label learners to predict all the respective labels. This is known as the MLL ensemble [13]. We propose a novel way to utilize aspect-based multi-label classification for sentiment classification of emotions in textual data. We compare 5 state of the art multi-label classification methods with 8 emotion-based textual data-sets.
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