Chapter 4 Case Study in Tmall.com & Taobao.com
4.1 Feature of Text information E-Commerce
In this chapter I introduce my own research about how to evaluate customer review data mining and using rough set approach to calculate the decision rules about product. The product I choose is cell phone. The product I choose is Samsung galaxy note III (三星) from taobao.com and tmall.com. With the price rate of 3000-4000 Rmb and take a review from 20 different sellers. This phone itself right now is in so many in the Chinese and worldwide market. First of all I all I select a view seller of Samsung galaxy note III from tmall.com and taobao.com (B2C). After that from the tmall.com and taobao.com seller page, I take some customers review about Samsung galaxy note III. From customer’s review from tmall.com and taobao.com, 20 sellers pick up. The example of the customers review is like example above:
Ex4.1 show customers review from taobao.com
Ex 4.2 show customers review from tmall.com
4.2 Analysis Procedure
Customer’s review data from taobao.com and tmall.com is taken. From collected 20 seller we collect some customer’s review data mining. Separate the noun and adjective from customer review. One by one is divided in two table. After separate noun and adjective, I do some survey to 20 people to find the synonym or similarity. From the similarity, I can eliminate some of the criteria of 26 criteria to 18 category. It can continue to next step of rough set based mining.
Customer’s review Data Mining system smooth software so good system is not smooth pretty good screen delicate screen average screen battery durable battery average pretty good packaging good call quality
Expensive prize
good accessories full accessories accessories genuin...
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...omers review from taobao.com and tmall.com, I give marking to each category (table 4.1). After rating it one by one by used the algebra formula it produced table 4.4 and table 4.5.
In the second experiment, is using the 4emkA2 software by input the data from the experiment (table 4.1). The result is shown in Figure 4.4. The use of the software is to fins the decision result. After using the software is found there is 11 generated decision rules (Figure 4.5). Finally the final process of 4emka2 software it is found that Figure 4.6 is reclassification result for the customers review mining.
Future research is needed to compare the classification abilities of this method in various situations with other case-based classification methods is needed to see some other result how to evaluate the customers review by using DRSA method and 4emka2 software decision result.
Healthcare: Sentiment analysis has wide-scale applications in the Healthcare industry. Many patients use internet to post their patient experience in provider facilities. This unbiased feedback from patients is critical for healthcare practices to improve the quality of care. It is not possible for the patient to keep going back to the facility to report post intervention feedback. Extracting patient sentiments from unstructured data in blogs, twitter, Facebook posts help hospitals realize important performance factors like patient satisfaction, staff friendliness, procedure efficiency. Patients also share information regarding their payer experience on the internet. Tweets, posts about insurance benefits, timely service are critical information to the payers to improve their existing services. 94% patients believe hospital’s brand name is important in making a selection. By understanding patient sentiments and taking appropriate action to translate negative feedback into improved care can help a hospital improve its brand image.
Second, Best buy would renovate its exist stores in different branch based on the more catalogue of customer who living in(Best Buy, 2015). Therefor, some stores is especially designed for a group of customer like A, some became Customer B stores, C stores. For instance, a delicates service stores with exclusive salesman for IT geek like Customer A. If Customer B, a soccer fans buys a headphone, he or she only want to ease his or her demand to listen music instead of the brand. However, Best Buy not only focus on current profit, but also focus on customer group interaction- to analysis customer purchase behavior (Best Buy, 2015). Accordingly, Best Buy would effectively estimate the next purchase and product combination from customer based on this method. For example, normally, a customer will purchase DVD after she or he buys a DVD player but Best buy’s customer profitability analysis indicated that this kinds of customer also buys music accessories in second day after she or he buy DVD. Thus Best Buy would be able to send more relevant e-mail and special combination offer specific customer after customer purchaser behavior
Classification Text documents are arranged into groups of pre-labeled class. Learning schemes learn through training text documents and efficiency of these system is tested by using test text documents. Common algorithms include decision tree learning, naive Bayesian classification, nearest neighbor and neural network. This is called supervised learning.
In order to further understand the customer data that has been accumulated through the purchases and transactions that have collected through the Nile online bookstore, three different regression models were completed. These regressions were completed in order to analyze the data in order to help the retailer better understand their customers, regarding which gender purchases more books, given the age of the customers or the day of the week the customer purchased a book. In order to help the retailer understand this information, customer data was gathered and used from over the last year in order to provide interpretations. The first step we week took to complete this process is to run the regressions, which resulted in three different models. We began by running the first regression. The first regression we ran contained only one variable, which was the gender variable. After running the first regression, we were able to successful complete
In the past decades, e-commerce is growing rapidly and prosperously which has led a number of research studies aimed at understanding how customers use a variety of factors such as pre-purchase factors and post-purchase factors to judge the quality of a website involved in e-commerce activity (Collier and Bienstock, 2006). Product return is a widespread and expensive problem among the whole world. In the online context, product returns are important metric and critical component to online retailers (Yalabik et al., 2005, De et al. 2013, Hong and Pavlou 2014), as they indicate problems in web site content, customer service, consumers’ buying experience or fulfillment operations. Nowadays, the development and innovations of mobile
eBay should continue to focus on growth and continue to fight off competition from rival
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My thesis contain the identification of accurately classifying the sentiment in text from micro blogs. This addresses the problem by retrieving opinions, performing processing on the data and analyzing the data using machine learning techniques to classify them by sentiment as positive, negative or neutral. I proposed sentimental natural language processing method for processing the text and use various machine learning algorithms and feature selection methods to determine the best approach. The approaches towards sentiment analysis are machine learning based methods, lexicon based methods and linguistic analysis. I proposed sentimental natural language processing Model for processing text to remove irrelevant features that do not affect its orientation. Sentimental natural language processing model carries opinions in natural language process as well as unstructured reviews with pointers, punctuations, emotions, repeated words, symbols, WH questions, URL’s are preprocessed to extract relevant features while sanitizing inputs. Sentimental natural language processing measures the importance of feature...
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