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K. Murugan

Bio: K. Murugan is an academic researcher from Madras Institute of Technology. The author has contributed to research in topics: Sentiment analysis. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Book ChapterDOI
03 Oct 2019
TL;DR: A framework is presented for mining online reviews extracted from by different reviewers & commercial websites and the potential applications of opinion mining rate the product by decision making, product analysis and improving business.
Abstract: Presently, social media are interactive and more user friendly in nature. The web users enable to provide a medium of exchange in analysing the opinionated comments of different reviewers. The review expressed in different commercial websites ranges from simple in form of sentence or paragraphs which is converted into graphical representation in form of star ratings. Commercial websites is interested to express the opinions in a broader sense driving as a revolution to e-commerce. Reviews expressed by the reviewers are also called as raters. On contrary, a huge people who are trustworthy provide fake reviews or bogus reviews to get their products. It is necessary to permit trusted people to review the products and post them on the web. Other approaches like trusted network, fixed machine address could be used for ranking the products. Since there are large number of people prefers to sell or buy products through e-commerce. The potential applications of opinion mining rate the product by decision making, product analysis and improving business. In this paper a framework is presented for mining online reviews extracted from by different reviewers & commercial websites.

3 citations


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Journal ArticleDOI
TL;DR: In this article , the authors extended the multi-index and multi-scale (MIMS) method into the generalized form, the online reviews are quantified by using the adverb structure scaling method, and an online reviews fusion method based on the improved TODIM (an acronym in Portuguese of interactive and multicriteria decision making) model is proposed.
Abstract: With the rapid development of computer networking technology, people pay more and more attention to the role of online reviews in management decision making. The existing methods of online reviews fusion are limited to rational decision-making behavior, which does not accord with the characteristics of evaluators’ behavior characteristics in the real environment. In order to solve the online reviews fusion problem based on bounded rational behavior which is closer to the reality of property service quality evaluation, the multi-index and multi-scale (MIMS) method is extended into the generalized form, the online reviews are quantified by using the adverb structure scaling method, and an online reviews fusion method based on the improved TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) model is proposed. The feasibility and effectiveness of the proposed method are verified by an example analysis of property service quality evaluation. The research results are as follows: the adverb structure scaling method is suitable for a large number of online reviews processing, the proposed method improves the efficiency of online reviews information fusion, and it is feasible and effective to evaluate property service quality based on the bounded rationality of evaluator’s behavior.

2 citations

OtherDOI
11 Feb 2022
TL;DR: In this paper , Map Reduce structure is utilized as information text information is spoken to as slant term grid with numerical qualities and a disseminated equal preparing structure is moreover proposed for online content examination to proficiently deal with huge information.
Abstract: Large information is a term that speaks to immense volumes of fast, perplexing, and variable information that require propelled methods and advances to empower the catch, stockpiling, the board, and investigation of the information. Because of attributes like volume, veracity, and speed, large information examination is getting one of the most testing research issues. Mining significant examples from monstrous information for dynamic, forecast, and so forth is at the center of huge information examination. This field of exploration consolidates text examination and Semantic Web advances. In spite of the fact that Map Reduce can give great versatility to cluster preparing, it is not proficient to deal with unstructured information like content information. A disseminated equal preparing structure is moreover proposed for online content examination to proficiently deal with huge information. This model speaks to semantic content rundown from electronic information with the assistance of FP-tree (Frequent Pattern tree) and semantic metaphysics utilizing the space information semantic. Here, Map Reduce structure is utilized as information text information is spoken to as slant term grid with numerical qualities.

1 citations

Book ChapterDOI
01 Jan 2021
TL;DR: A novel influence maximization technique, namely the DegGreedy technique, has been illustrated along with experimental results to make a comparative analysis of the existing techniques.
Abstract: Influence maximization in online social networks (OSNs) is the problem of discovering few nodes or users in the social network termed as ‘seed nodes', which can help the spread of influence in the network. With the tremendous growth in social networking, the influence exerted by users of a social network on other online users has caught the attention of researchers to develop effective influence maximization algorithms to be applied in the field of business strategies. The main application of influence maximization is promoting the product to a set of users. However, a real challenge in influence maximization algorithms to deal with enormous amount of users or nodes obtainable in any OSN is posed. The authors focused on graph mining of OSNs for generating ‘seed sets' using standard influence maximization techniques. Many standard influence maximization models are used for calculation of spread of influence; a novel influence maximization technique, namely the DegGreedy technique, has been illustrated along with experimental results to make a comparative analysis of the existing techniques.