000			03291nam a22002897a 4500
003			KOHA
005			20231030090942.0
008			231030d2023 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3163 _bH25 2023
100	1		_aHakizimana, Lyse Deborah
245	1	0	_aEVALUATING CUSTOMER SATISFACTION USING MACHINE LEARNING TECHNIQUES / _cLYSE DEBORAH HAKIZIMANA; SUPERVISOR: ASST. PROF. DR KIAN JAZAYERI
246	2	3	_aCASE STUDY OF U.S. AIRLINE INDUSTRY
264			_c2023
300			_avii, 51 sheets; _c31 cm. _e1 CD-ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department
504			_aIncludes bibliography (sheets 50-51)
520			_aABSTRACT In today's industry environment, the airline company is increasingly focused on comprehending and responding to client needs and feedback to improve general levels of satisfaction. By measuring tweets sentiment, airlines can win a complete comprehension of customer‟s sentiments and opinions which can help airline industries in the creation of quality improvement plans. In this study, we used sentiment analysis, one of which understands text-sensitive applications, to find consumers‟ opinions. Machine learning algorithms have been developed for categorizing Twitter postings as positive, negative, or neutral. We trained our model on tweets from six US airlines, and this study explores the use of predictive analytics using the Naive Bayes algorithm which is a useful tool for extracting useful insights. The text data is cleaned using preprocessing techniques to ensure adequate input for the Naive Bayes classifier. Random Forest classifier and the K-Nearest Neighbors algorithm were combined with a model constraint classifier to increase the accuracy of Naive Bayes after selecting the most essential features using SelectKBest. This study examines the accuracy of Naive Bayes used for sentiment classification in Twitter data. The findings and insights can be utilized to discover effective practices as well as areas for more investment in order to improve customer satisfaction. The algorithm's probabilistic approach can classify tweets in negative, neutral, or positive sentiments by allowing airlines to identify areas of customer complaint and areas of excellence. In this work, the performance parameters of the proposed solution were considered to be recall, precision, and F1-score. Naive Bayes classifier shows a capacity to correctly categorize the opinions conveyed through tweets. The collected findings offer light on common consumer complaints, allowing airlines to promptly resolve issues and target initiatives to increase success. Keywords: Customer satisfaction, K-Nearest Neighbors, Naive Bayes, Random Forest, Select KBest, Sentiment Analysis, Stacking Model, Twitter Data and US Airline
650		0	_aConsumer satisfaction _vDissertations, Academic
650		0	_aSentiment analysis _vDissertations, Academic
700	1		_aJazayeri, Kian _esupervisor
942			_2ddc _cTS
999			_c291578 _d291578