A New Approach to Handling Erroneous Reviews in Opinion Mining - Kushwah, Kirti; Gupta, Rajendra Kumar (International Journal of Computer Sciences and Engineering, 2019)
In the areas of marketing and electronic advertising, Opinion Mining has a broader domain. The advertiser must analyze the performance / popularity of the advertisements he has posted on the site. The mechanism based on the star rating can be fraudulent, due to robots or automatic responders. Therefore, it is necessary to analyze the current entity system or products using reviews (comments). Opinion Mining refers to the extraction of those lines or phrases in the huge raw data that express an opinion. On the other hand Sentimental Analysis is the analysis of feelings identifies the polarity (sentiment) of the opinion that is extracted from the review. Today, social networking sites and online shopping sites are used by users to express their opinion on products, events, peoples etc. Many users that express their opinion regarding any entity/Product, there may be chances that reviews are not written in correct form (Dictionary). Because of reviews available on these sites may contain noise such as spelling errors, typographical errors, standard abbreviations, and elegant writing. It is necessary to make data noise-free so that it can be used for opinion extraction. This paper describe a framework that was proposed to conduct opinion analysis of noisy reviews using techniques such as calculate similarity of terms and frequency of the document. The reviews of different products have been tested by this framework and the corresponding result is shown in negative (-ve) and positive (+ve ) form. The results are satisfactory for all the tested products.