MARC ayrıntıları
| 000 -BAŞLIK |
|---|
| Sabit Uzunluktaki Kontrol Alanı |
03291nam a22002897a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ |
|---|
| Kontrol Alanı |
KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI |
|---|
| Kontrol Alanı |
20231030090942.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ |
|---|
| Sabit Alan |
231030d2023 cy ||||| m||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI |
|---|
| Özgün Kataloglama Kurumu |
CY-NiCIU |
| Kataloglama Dili |
eng |
| Çeviri Kurumu |
CY-NiCIU |
| Açıklama Kuralları |
rda |
| 041 ## - DİL KODU |
|---|
| Metin ya da ses kaydının dil kodu |
eng |
| 090 ## - Yerel Tasnif No |
|---|
| tasnif no |
YL 3163 |
| Cutter no |
H25 2023 |
| 100 1# - KİŞİ ADI |
|---|
| Yazar Adı (Kişi adı) |
Hakizimana, Lyse Deborah |
| 245 10 - ESER ADI BİLDİRİMİ |
|---|
| Başlık |
EVALUATING CUSTOMER SATISFACTION USING MACHINE LEARNING TECHNIQUES / |
| Sorumluluk Bildirimi |
LYSE DEBORAH HAKIZIMANA; SUPERVISOR: ASST. PROF. DR KIAN JAZAYERI |
| 246 23 - DEĞİŞİK BAŞLIK FORMU |
|---|
| Başlık uygun / kısa başlık |
CASE STUDY OF U.S. AIRLINE INDUSTRY |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 300 ## - FİZİKSEL TANIMLAMA |
|---|
| Sayfa, Cilt vb. |
vii, 51 sheets; |
| Boyutları |
31 cm. |
| Birlikteki Materyal |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| Media type code |
n |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| Carrier type code |
nc |
| 502 ## - TEZ NOTU |
|---|
| Tez Notu |
Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department |
| 504 ## - BİBLİYOGRAFİ NOTU |
|---|
| Bibliyografi Notu |
Includes bibliography (sheets 50-51) |
| 520 ## - ÖZET NOTU |
|---|
| Özet notu |
ABSTRACT<br/>In today's industry environment, the airline company is increasingly focused on <br/>comprehending and responding to client needs and feedback to improve general <br/>levels of satisfaction. By measuring tweets sentiment, airlines can win a complete <br/>comprehension of customer‟s sentiments and opinions which can help airline <br/>industries in the creation of quality improvement plans. In this study, we used <br/>sentiment analysis, one of which understands text-sensitive applications, to find <br/>consumers‟ opinions. Machine learning algorithms have been developed for <br/>categorizing Twitter postings as positive, negative, or neutral. We trained our model <br/>on tweets from six US airlines, and this study explores the use of predictive analytics <br/>using the Naive Bayes algorithm which is a useful tool for extracting useful insights. <br/>The text data is cleaned using preprocessing techniques to ensure adequate input for <br/>the Naive Bayes classifier. Random Forest classifier and the K-Nearest Neighbors <br/>algorithm were combined with a model constraint classifier to increase the accuracy <br/>of Naive Bayes after selecting the most essential features using SelectKBest. This <br/>study examines the accuracy of Naive Bayes used for sentiment classification in <br/>Twitter data. The findings and insights can be utilized to discover effective practices <br/>as well as areas for more investment in order to improve customer satisfaction. The <br/>algorithm's probabilistic approach can classify tweets in negative, neutral, or positive <br/>sentiments by allowing airlines to identify areas of customer complaint and areas of <br/>excellence. In this work, the performance parameters of the proposed solution were <br/>considered to be recall, precision, and F1-score. Naive Bayes classifier shows a <br/>capacity to correctly categorize the opinions conveyed through tweets. The collected <br/>findings offer light on common consumer complaints, allowing airlines to promptly <br/>resolve issues and target initiatives to increase success.<br/>Keywords: Customer satisfaction, K-Nearest Neighbors, Naive Bayes, Random <br/>Forest, Select KBest, Sentiment Analysis, Stacking Model, Twitter Data and US <br/>Airline |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Consumer satisfaction |
| Alt başlık biçimi |
Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Sentiment analysis |
| Alt başlık biçimi |
Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI |
|---|
| Yazar Adı (Kişi adı) |
Jazayeri, Kian |
| İlişkili Terim |
supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) |
|---|
| Sınıflama Kaynağı |
Dewey Onlu Sınıflama Sistemi |
| Materyal Türü |
Thesis |