Abstract
Opinion target extraction is one of the core tasks in sentiment analysis on text data. In recent years, dependency parser–based approaches have been commonly studied for opinion target extraction. However, dependency parsers are limited by language and grammatical constraints. Therefore, in this work, a sequential pattern-based rule mining model, which does not have such constraints, is proposed for cross-domain opinion target extraction from product reviews in unknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also reveals the difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the first stage, the aspects of reviews are extracted from the target domain using the rules automatically generated from source domains. The aspects are also transferred from the source domains to a target domain. Moreover, aspect pruning is applied to further improve the performance of aspect extraction. In the second stage, the opinion target is extracted among the aspects extracted at the former stage using the rules automatically generated for opinion target extraction. The proposed model was evaluated on several benchmark datasets in different domains and compared against the literature. The experimental results revealed that the opinion targets of the reviews in unknown domains can be extracted with higher accuracy than those of the previous works.
Highlights
The experimental results revealed that the opinion targets of the reviews in unknown domains can be extracted with higher accuracy than those of the previous works
Since the number of works, which addresses the unknown target domain in cross-domain studies for opinion target extraction, is quite limited in the literature, the experimental results were compared against a recent cross-domain extraction model, namely CD-ALPHN [8] and five well-known classifiers including decision tree (DT), k-nearest neighbor, multi-layer perceptron (MLP), naïve Bayes (NB), and support vector machine (SVM)
The best results for the aspect extraction and opinion target extraction are presented in Tab. 12
Summary
People can share their ideas through the Internet and have the opportunity to benefit from other people's ideas. With the influence of social media, people make more comments on products, services, or anything. Appropriate analysis and interpretation of these comments play an important role in decision-making regarding a topic or problem. Called opinion mining, aims to make a detailed analysis of sentiments, or opinions, about products and services [1–3]. Sentiment analysis is carried out at three different levels: document, sentence, and aspect level [4]. Sentiment analysis at the document level aims to find the dominant opinion in the document. Each sentence of the document is handled separately and a subjectivity analysis of the
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