Different clustering techniques : means for improved knowledge discovery

Application of different clustering techniques can result in different basic data set partitions emphasizing diversified aspects of resulting clusters. Since analysts have a great responsibility for the successful interpretation of the results obtained through some of the available tools, and for giving meaning to what forms a qualitative set of clusters, additional information attained from different tools is of a great use to them. In this article we presented the clustering results of small and medium sized enterprises’ (SMEs) data, obtained in DataEngine, iData Analyzer and Weka tools for intelligent analysis

SAVREMENE TEHNIKE ANALIZE PODATAKA ZA MENADŽMENT ONLAJN REPUTACIJE U HOTELIJERSTVU I TURIZMU

Knowing what attracts or deters tourists to/from a tourist visit and what products to offer them and to pay special attention to is crucial for good economic results. Such knowledge can be obtained by analysis of online comments and reviews that tourists leave on travel websites (such as Booking, TripAdvisor, Trivago, etc.). This paper describes the value which information about opinions and emotions hidden in online reviews has for managers who receive it, especially the knowledge of (dis)satisfaction of users with certain aspects of the tourist offer. Uncovered knowledge from online reviews provides a chance to take advantage of the strong points, and correct the shortcomings through timely corrective measures and actions. Contemporary approaches and methods of analyzing online reviews and the opportunities for development they provide in the tourism industry are described through a case study conducted over a subset of 20491 hotel reviews from TripAdvisor. We have conducted sentiment analysis of reviews with the goal of building an automated model which will successfully distinguish positive from negative reviews. Logistic Regression classifier has the best performance, in 90% of reviews it has correctly classified positive reviews and in 83% negative. We have illustrated how association rules can help management to uncover relationships between concepts under discussion in negative and positive reviews.Saznanja o tome šta privlači a šta odvraća turiste od turističke posete i na koje proizvode obratiti posebnu pažnju, te koje proizvode ponuditi je od presudne važnosti za ostvarivanje dobrih ekonomskih rezultata. Do saznanja ove vrste možemo doći analizom onlajn komentara i recenzija koje savremeni turisti ostavljaju nakon turističkog iskustva na veb sajtovim (kao što su Booking.com, TripAdvisor, Trivago, i dr.). U radu je opisan značaj onlajn recenzija za menadžment, koji putem njih dobija informaciju o mišljenjima i emocijama korisnika njihovih turističkih usluga, a pogotovu o (ne)zadovoljstvu određenim aspektima ponude, te se pruža mogućnost da iskoriste uočene prednosti, a isprave nedostatke preduzimanjem pravovremenih korektivnih mera i akcija. Kroz studiju slučaja nad 20491 recenzijom sa TripAdvisor-a su opisani savremeni pristupi i metode za analizu korisnički generisanog sadržaja i mogućnosti za unapređenje koje one donose u domenu hotelijerstva i turizma. Realizovana je sentiment analiza nad prikupljenim onlajn recenzijama sa ciljem izgradnje automatizovanog modela koji uspešno pravi razliku između pozitivnih i negativnih recenzija. Klasifikacioni model zasnovan na logističkoj regresiji ispoljava najbolje performanse. U 90% slučajeva uspešno klasifikuje pozitivne recenzije, dok u 83% slučajeva uspešno klasifikuje negativne. Pored primene sentiment analize, ilustrovana je upotreba asocijativnih pravila kao pomoć menadžmentu u otkrivanju relacija između koncepata o kojima posetioci diskutuju unutar pozitivnih, odnosno negativnih recenzija