Paper Info
Title
On the Integration of Deep Learning and Fuzzy Methods for Aspect-based Sentiment Analysis.
Abstract
Sentiment analysis is an important task in the field of big data and artificial intelligence and has a wide range of real-world applications. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, or text span, irrespective of the entities mentioned and their aspects. A typical way for solve this is to employ a classifier that learns to identify the polarity labels given input text. Such methods require amount of training examples which were manually constructed. While most work in text mining in the field of sentiment analysis domain focus on the use of supervised machine learning technologies, in this paper, we investigate on aspect-based sentiment analysis for mobile game reviews. Our method integrates both deep learning learned features and support vector machines. The deep learning feature is mainly derived from inducting word relations in Wikipedia and the collected in-domain text. To prevent from assessing unknown missing words, we design a fuzzy-based approach to capture word-level sentiment scores. By using a fuzzy-based soft computing strategy with a small lexicon allows us to produce a system with better accuracy and precision than pure machine learning such as Naive Bayes and SVMs. To demonstrate the effectiveness of the proposed approach, we conduct the experimental results on the collected Mobile game reviews. These results show that our method outperforms supervised systems. One good property of this method is that it does not need to perform Chinese word segmentation in testing time.
Year
DOI
Venue
2019
10.1109/IIAI-AAI.2019.00105
IIAI-AAI
Field
DocType
Citations 
Naive Bayes classifier,Sentiment analysis,Computer science,Fuzzy logic,Support vector machine,Artificial intelligence,Soft computing,Deep learning,Classifier (linguistics),Big data,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Yu-Chieh Wu124723.16
Jie-Chi Yang200.34