Abstract
User reviews, blogs, and social media data are widely used for various types of decision-making. In this connection, Machine Learning and Natural Language Processing techniques are employed to automate the process of opinion extraction and summarization. We have studied different techniques of opinion mining and found that the extraction of opinion target and opinion words and the relation identification between them are the main tasks of state-of-the-art techniques. Furthermore, domain-independent features extraction is still a challenging task, since it is costly to manually create an extensive list of features for every domain. In this study, we tested different syntactic patterns and semantic rules for the identification of evaluative expressions containing relevant target features and opinion. We have proposed a domain-independent framework that consists of two phases. First, we extract Best Fit Examples (BFE) consisting of short sentences and candidate phrases and in the second phase, pruning is employed to filter the candidate opinion targets and opinion words. The results of the proposed model are significant.
Highlights
The increasing trend of social media usage on the internet and mobiles has amazingly diverted the ways of business, marketing, and other day to day activities
We have focused on the sentence level domain independent extraction of opinion words and opinion targets based on the grammatical structure of the sentences
Best Fit Examples (BFE) pruning pertains to refining target features extracted through a best-fit fit examples algorithm, while
Summary
The increasing trend of social media usage on the internet and mobiles has amazingly diverted the ways of business, marketing, and other day to day activities. Huge data is floated on the web and is most popularly used in tremendous ways for various applications. Social media data is used by customers, businesses, manufacturers, and administrative machinery for decision-making and e-governance. Opinion mining from customer reviews has become an interesting topic of research. The data sciences techniques and data-driven predictive models are most popularly employed to automate this process [1]. Opinion extraction can be carried out in two ways, i.e., one way is to obtain the overall summary of sentiment about a product and the other way is to produce a feature-based opinion summary about a product referred to as fine-grained analysis
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