How to analyze the customer complaints ecommerce data science?
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Keywords: Information management, Data mining, Text mining, Web mining, heating systems firms, managing customer complaints Abstract. The amount and the complexity of Web pages have been increasing explosively, as has the information contained within Web pages. In today’s world, firms’ Web data must be analyzed to gain competitive advantage in the topic sector. Web text mining (TM) is gaining a lot of importance because of being used increasingly in business applications for understanding and predicting valuable information. It plays a key role in organizing huge amount of web unstructured (textual) data and condensing it into valuable knowledge. Customer complaints give businesses valuable information about how they need to improve. This paper addresses a Web TM application to extract useful, interesting and hidden knowledge to implement heating systems firms’ in competition. Top seven heating systems firms are analyzed about customers’ complaints in Turkey. Data are collected from a complaint Website with RapidMiner Web Mining Tool. Then data is transformed to a collection of documents by generating a document for each record. Every complaint is transformed to a document. These documents which are collected for one years’ time between 2012 december and 2013 october are analyzed with TM techniques. Summarization, tokenization, stemming, and filtering are also used. In addition, the similarities of firms about the subject are determined. Not only have we analyzed the customers in the sector but also the firms about the complaints. 1. Introduction The world has turned into a small village. In addition, the price advantage prevails in the world of sales nowadays. The only way to stand out in the competition lies in the aftermarket. In order to satisfy the customer we must analyze all kinds of data. Because social media and Internet media put consumer on a stronger location. Customers are really kings in the big data world. Customer satisfaction is not an absolute scenario, but very much depends on interactions, feedback, praise, and complaints. Complaints have to be looked at in a constructive, positive and professional perspective [1
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Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions
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