Automatically identifying informative reviews is increasingly important given the rapid growth of user generated reviews on sites like Amazon and TripAdvisor. In this paper, we describe and evaluate techniques for identifying and recommending helpful product reviews using a combination of review features, including topical and sentiment information, mined from a review corpus.

Bu çalışmanın amacı dünyanın en prestijli restoran derecelendirme sistemi olan ve lüks restoranları puanlayan yıldız veren Michelin Rehberi tarafından Türkiye’de ilk defa yıldızlarla ödüllendirilen İstanbul’da bulunan 5 işletmesinin Tripadvisor’da son 3 yıl içerisinde (2020/2021/2022) yapılan yorumlarının içerik analiziyle inceleyerek, yerli yabancı müşterilerin olumlu olumsuz deneyimlerinin ne...

Sentiment Analysis (SA) of reviews refers to the task of analyzing natural language text in forums like Amazon, TripAdvisor, Yelp, IMDB etc. to obtain the writer’s feelings, attitudes, and emotions expressed therein towards a particular topic, product, or entity. It involves overlapping approaches in several domains like Natural Language Processing (NLP), Computational Linguistics (CL), Informa...

In this paper, we describe a novel data model for particular online business social networks such as Tripadvisor and Yelp: we also define a greedy influence maximization algorithm to determine the most influential users on the base of proper influence patterns. The result of such analysis is then combined with some economic data in order to propose a set of possible financial strategies for bus...

Consumers frequently rely on user-generated product reviews to guide purchasing decisions. Given the everincreasing volume of such reviews and variations in review quality, consumers require assistance to effectively leverage this vast information source. In this paper, we examine to what extent the readability of reviews is a predictor of review helpfulness. Using a supervised classification a...