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Thesis . 2016
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Product Search Engine Using Product Name Recognition and Sentiment Analysis

Authors: Gözükara, Furkan;

Product Search Engine Using Product Name Recognition and Sentiment Analysis

Abstract

Bu çalışmada, odaklı tarayıcı, kayıt e ş leştirme ve duygu analizi sistemlerinden oluşan özgün bir ürün arama motoru önerilmiştir. E-ticaret siteleri için özgün bir odaklı tarayıcı geliştirilmiş ve bu süreçteki zorluklar ve bu zorluklara önerilen çözüm önerileri detaylı bir şekilde sunulmuştur. E-ticaret ürünlerine yapılan yorumları olumlu ve olumsuz şeklinde sınıflandıracak bir duygu analizi sistemi geliştirilmiştir. Farklı E-ticaret sitelerinden toplanmış aynı ürün isimlerini tanıyacak özgün bir kayıt eşleştirme sistemi sunulmuştur. Kayıt eşleştirme sistemi, dinamik/artımsal olacak şekilde değiştirilmiş Hiyerarşik Tümevarım Kümeleme algoritması üzerine kurulmuştur ve önerdiğimiz ürün kodu eşleştirme sistemi ile kümeleme yaparken ürün ismi karşılaştırma sayısını oldukça azaltmaktadır. Bu sistemlere ek olarak, ürün arama motoru için bir arama sistemi ve kullanıcı ara yüzü de geliştirilmiştir. Bu tez çalışmasında, web taramasında %472 performans artışı, duygu analizinde %91.08 doğruluk, kayıt eşleştirme sisteminde %96.25 Fölçütü, ve en alakalı ürünleri arama sisteminde %100 isabet elde eden tam kapsamlı bir ürün arama motoru sunulmuştur. Önerilen sistem mevcut sistemlerden daha iyi bir kullanıcı deneyimi sunmayı başarmaktadır.

In this study, a novel product search engine system which consists of a focused crawler, a record linkage system and a sentiment analyzer is proposed. We develop an original focused web crawler for E-commerce sites, and the challenges and our proposed solutions are presented in detail. A sentiment analyzer is developed to classify E-commerce product comments into polarities as negative or positive. A novel record linkage system for E-commerce products is proposed to recognize the same product names collected from different E-commerce sites. The record linkage system is based on a modified dynamic/incremental Hierarchical Agglomerative Clustering algorithm which employs our proposed product code matching system to reduce number of product name comparisons during clustering. In addition to these systems, a search system and a user interface are developed for the product search engine. In this thesis, we present a full scale product search engine that obtains %472 performance boosts in the crawler, 91.08% accuracy in the sentiment analysis, 96.25% F-measure in the record linkage, and 100% precision in most related products search. The proposed system achieves to provide better user experience than the existing systems.

Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi tarafından desteklenmiştir. Proje No: 2211-C.

Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2016.

XXI, 212 s. :_res. (gnl. rnk.), tablo ;_29 cm.

Kaynakça (s. 181-202) var.

TEZ12432

Country
Turkey
Related Organizations
Keywords

Ürün Kümeleme, Duygu Analizi, Computer Engineering and Computer Science and Control, Hierarchical clustering, Product Clustering, Veri Madenciliği, Odaklı Web Tarayıcı, Sentiment Analysis, Data Mining, Record Linkage, Kayıt Eşleştirme, Focused Web Crawler, Data mining, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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