000			03095ntm a2200325Ia 4500
001			318246
005			20260707210610.0
008			051012s2017 xx a bm 000 0 eng d
040			_aORE _cORE
049			_aOREV
090			_aLD4330 _bChebet, Patricia
100	1		_aChebet, Patricia. _9407514
245	1	2	_aA Mobile application for real time analysis of customer feedback in banks based on Naive Bayes model / _cby Patricia Chebet.
260			_cc2017.
300			_axx leaves : _bill. ; _c29 cm.
500			_aPrintout.
502			_aThesis ()--Oregon State University, .
520	3		_aWithin a week of poor customer service, customers move to a competitor's business. When this happens in large scale, the company is at risk of closing down or experiencing extreme losses. This is so because losing existing customers means decreased sales and degrading of brand reputation, as disappointed customers will spread the word, discouraging potential customers from that brand. This clearly indicates that customer satisfaction is a critical factor that dictates whether a company will be a success or not. Banks need to implement strategies that can help them predict customer issues before they escalate. These strategies should enable these banks to identify and solve any slight change in customer behavior in real-time. This study focuses on developing an application that is usable by banks to prompt customers for and analyse customer feedback to understand the customer's opinion about their service using a mobile application. Currently, in banks, a customer leaves a note of how he or she views the banks services or give feedback using the "One click feedback" strategy that requires a customer to rank or rate the service to know their customers view about their services. In the proposed mobile application, real-time analysis of customer feedback is achieved through the utilisation of the Naive Bayes model. This model relies on the assumption that every feature, in this study each word in a review being classified, is independent of any other feature. To classify text to a polarity of either positive, negative or neutral, this model requires two data sets; a training set and a test set. The training set is used to train the model to classify texts and it had two files, one made up ¾ of 60 positive reviews and the other made up ¾ of 60 negative reviews. The test set is used to estimate the accuracy of the model and makes up ¼ of each the positive and negative data sets. The mobile application was developed using Agile Software Development Methodology and runs on Android platform.
530			_aAlso available on the World Wide Web.
504			_aIncludes bibliographical references (leaves - ).
650		0	_aOpinion Mining. _9415215
650		0	_aSentiment Analysis. _9409773
650		0	_aText Classification. _9415216
650		0	_aNaive Bayes. _9415217
650		0	_aMobile Application.
650		0	_aCustomer Feedback. _9415218
856			_uhttp://hdl.handle.net/11071/5697
942			_cTH
999			_c318246 _d289502