Online social networking services and spam detection approaches in opinion mining - a review
Meesala Shobha Rani, Sumathy Subramanian · International Journal of Web Based Communities · 2018
Social network platforms have gained enormous growth in present days. Users are more attracted towards social network sites such as Twitter, Facebook, micro blogs and YouTube to express their opinion and views and share the knowledge. With the tremendous increase in technological growth and with the diversity of products, online e-commerce servicing sites are becoming competitor's for each other to increase their proliferation in business offering to post positive product spam reviews. Users generally express their opinion based on different sentiment orientations, ratings and the features of the product. This tends to create ambiguity at the customers end in arriving at a decision based on the criticism, building fake opinion on the products. Opinion spam detection provides scope in building opinion from the reviews of various networking sites. A novel framework using meta-heuristic and k-means clustering approach is proposed for identifying opinion spam detection using flower pollination, grey wolf and moth flame. Amazon automotive product dataset is chosen for analysis and it is observed that grey wolf algorithm performs better than flower pollination and moth flame optimisation algorithms in terms of improved convergence speed, mean, standard deviation, variance and elapsed run time.