scholarly journals ¬¬¬¬¬KLASIFIKASI KELUHAN PELANGGAN BERDASARKAN TINGKAT PENANGANAN PADA PERUSAHAAN LAYANAN INTERNET MENGGUNAKAN ROCCHIO CLASSIFIER

JOUTICA ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 206
Author(s):  
Wildan Suharso ◽  
Hardianto Wibowo
Keyword(s):  
Text Mining ◽  
Customer Service ◽  
Data Preprocessing ◽  
Features Selection ◽  
Customer Churn

Banyak penelitian yang dilakukan untuk meneliti perusahaan layanan internet di Indonesia mulai dari penggunaan database, customer churn hingga penyelarasan tujuan teknologi informasi. Penelitian yang dilakukan oleh Suharso tahun 2013 menyatakan bahwa pelanggan tidak akan berpindah pada perusahaan pesaing jika pengguna merasa nyaman dengan layanan yang diberikan, namun permasalahan pelanggan tidak hanya disebabkan oleh layanan, aplikasi yang mendukung turut mendukung proses bisnis yang dilakukan seperti yang telah dijelaskan oleh Suharso pada tahun 2016. Salah satu aplikasi yang harus dibangun pada ISP adalah aplikasi yang memudahkan pegawai perusahaan layanan internet atau ISP dalam menyelesaikan keluhan pelanggan karena ditemukan keragaman penyelesaian permasalahan sehingga menyebabkan kurang standarnya waktu penyelesaian. Pendekatan text mining dilakukan untuk meminimalisir permasalahan dengan menggunakan rocchio classifier yang terdiri dari beberapa tahapan antara lain studi pustaka, pengumpulan data, preprocessing, features selection, rocchio classifier, dan analisis hasil. Hasil yang diperoleh antara lain waktu rata rata proses parsing adalah 0,0333 detik, rata-rata proses klasifikasi adalah 0,0905 detik, dan rata-rata keseluruhan proses adalah 0,1238 detik, selain secara fungsional aplikasi dapat membantu customer service dalam menentukan tingkat penanganan masalah.

Understanding retail quality of sporting goods stores: a text mining approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luke Lunhua Mao
Keyword(s):  
Text Mining ◽  
Customer Service ◽  
Online Reviews ◽  
The United States ◽  
Alternative Methods ◽  
Content Type ◽  
Sport Industry ◽  
Sporting Goods ◽  
Quality Dimensions

PurposeSporting goods retailing is a significant sector within the sport industry with the total revenue of this sector reaching $52.2 billion in 2018. Beset with formidable competition, sporting goods stores are compelled to augment their merchandise with service and improve retail quality. The purpose of this study is to investigate retail quality of sporting goods stores (RQSGS).Design/methodology/approachBased on 27,793 online reviews of 1481 stores in the United States, this study used Leximancer 4.0, a text mining software, to identify critical retail quality dimensions associated with sporting goods stores, and further explored the most salient dimensions among different levels of ratings.FindingsCustomer service and store aspects are the two higher-order dimensions of RQSGS; holistic experience, manager and staff are three themes under customer service, and product, B&M store and online–offline integration are three themes under store aspects. Furthermore, extreme reviews focus more on customer service, whereas lukewarm reviews focus more on store aspects.Practical implicationsKnowledgeable staff, managers and online–offline integration are instrumental in creating superior retail quality. Sporting goods stores should enhance hedonic and social values for consumers in order to ward off online competitions.Originality/valueThis study explored retail quality dimensions that are pertinent to sporting goods retailing utilizing text mining methods. This study to certain extent cross-validated the existing retailing literature that is developed on alternative methods.

Gaining customer knowledge in low cost airlines through text mining

2014 ◽  
Vol 114 (9) ◽  
pp. 1344-1359 ◽  
Author(s):  
Bee Yee Liau ◽  
Pei Pei Tan
Keyword(s):  
Text Mining ◽  
Customer Service ◽  
Design Methodology ◽  
Low Cost ◽  
Negative Polarity ◽  
Statistical Techniques ◽  
Content Type ◽  
Low Cost Carriers ◽  
Flight Cancellations ◽  
Practical Implications

Purpose – The purpose of this paper is to study the consumer opinion towards the low-cost airlines or low-cost carriers (LCCs) (these two terms are used interchangeably) industry in Malaysia to better understand consumers’ needs and to provide better services. Sentiment analysis is undertaken in revealing current customers’ satisfaction level towards low-cost airlines. Design/methodology/approach – About 10,895 tweets (data collected for two and a half months) are analysed. Text mining techniques are used during data pre-processing and a mixture of statistical techniques are used to segment the customers’ opinion. Findings – The results with two different sentiment algorithms show that there is more positive than negative polarity across the different algorithms. Clustering results show that both K-Means and spherical K-Means algorithms delivered similar results and the four main topics that are discussed by the consumers on Twitter are customer service, LCCs tickets promotions, flight cancellations and delays and post-booking management. Practical implications – Gaining knowledge of customer sentiments as well as improvements on the four main topics discussed in this study, i.e. customer service, LCCs tickets promotions, flight cancellations or delays and post-booking management will help LCCs to attract more customers and generate more profits. Originality/value – This paper provides useful insights on customers’ sentiments and opinions towards LCCs by utilizing social media information.

scholarly journals Research of Clustering Algorithms using Enhanced Feature Selection

2019 ◽  
Vol 9 (2) ◽  
pp. 4612-4615
Keyword(s):  
Feature Selection ◽  
Text Mining ◽  
Clustering Algorithms ◽  
Present Situation ◽  
Features Selection ◽  
Entity Extraction ◽  
Text Documents ◽  
Clustering Techniques ◽  
Selection For ◽  
Video And Audio

In Present situation, a huge quantity of data is recorded in variety of forms like text, image, video, and audio and is estimated to enhance in future. The major tasks related to text are entity extraction, information extraction, entity relation modeling, document summarization are performed by using text mining. This paper main focus is on document clustering, a sub task of text mining and to measure the performance of different clustering techniques. In this paper we are using an enhanced features selection for clustering of text documents to prove that it produces better results compared to traditional feature selection.

Defecting wine club members: an exploratory study

2018 ◽  
Vol 30 (3) ◽  
pp. 309-330 ◽  
Author(s):  
Sandra K. Newton ◽  
Linda I. Nowak ◽  
Mayuresh Kelkar
Keyword(s):  
Product Quality ◽  
Customer Service ◽  
Online Survey ◽  
Fair Value ◽  
Good News ◽  
Switching Behavior ◽  
Original Research ◽  
Variety Seeking ◽  
Content Type ◽  
Customer Churn

Purpose The purpose of this study is to investigate the range of explanations for why wine club members defect and move on. Design/methodology/approach This quantitative research study uses data from US wine consumers, gathered through an online survey of 399 former wine club members who had quit their membership in the recent past. Consistent with literature on customer churn rates in subscription markets, data are analyzed using descriptive statistics, factor analysis, hierarchical multiple regression and analysis of variance. Findings The results reported by respondents indicate that higher levels of perceived product quality, fair value in pricing, variety seeking and commitment to customer service at the beginning and at the end of a wine club membership lead to higher levels of customer satisfaction and a desire to recommend the club to others even after quitting. Though variety seeking is more commonplace among experienced wine drinkers, the good news for wineries is that consumers are more likely to recommend a wine club to others if at least a year has passed after they decided to quit. Practical implications The results provide implications for wine club managers seeking to improve wine club retention with suggested means for mitigating the rate of customer attrition. Originality/value This paper presents original research addressing a variety of reasons why wine club members quit. The extant research has found that factors such as product quality, fair pricing, service commitments and variety-seeking behavior affect members’ satisfaction with their wine club, as well as their desire to recommend it to others. The authors have attempted to combine all these factors into a single study to gain insight into wine club members’ switching behavior, and to find out what the wineries can do to improve customer loyalty.

scholarly journals THE INVESTIGATION OF ONLINE CUSTOMER COMPLAINTS IN THE BANKING SECTOR BY TEXT MINING

2020 ◽  
Vol 8 (5) ◽  
pp. 3835-3866
Author(s):  
Gamze YILDIZ ERDURAN ◽  
Fatma LORCU
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Customer Service ◽  
Credit Card ◽  
Banking Sector ◽  
Retail Banking ◽  
Mining Method ◽  
Clustering Method ◽  
Customer Complaints ◽  
Data Mining Method

The goal of this study is to obtain new gains that would provide benefits to businesses from customer complaints that customers offer voluntarily and free of charge. In line with this purpose, in this study, 25,390 online customer complaints concerning banks operating in the retail banking sector in Turkey were analysed by data mining method. By using the clustering method in data mining analysis, complaints were grouped, familiar words, similar or the words used together of the complaints were identified. As a result of the analysis done, the most frequently mentioned banks among customer complaints and the subjects that customers complained about most were determined. It was revealed that the subjects that the bank customers complain about most within the relevant periods were “branch, credit card fee, cancellation, customer service, subscription fee”. Also, the result emerged that bank customers used the words “unfair” and “victimisation” when expressing their dissatisfaction.

scholarly journals A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

2021 ◽  
Author(s):  
Mourad Ellouze ◽  
Seifeddine Mechti ◽  
Moez Krichen ◽  
vinayakumar R ◽  
Lamia Hadrich Belguith
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Text Mining ◽  
Personality Disorders ◽  
Deep Neural Network ◽  
Selection Task ◽  
Learning Approach ◽  
Features Selection ◽  
Normal Behavior

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

Dimensionality and data reduction in telecom churn prediction

Kybernetes ◽  
2014 ◽  
Vol 43 (5) ◽  
pp. 737-749 ◽  
Author(s):  
Wei-Chao Lin ◽  
Chih-Fong Tsai ◽  
Shih-Wen Ke
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Data Reduction ◽  
Data Preprocessing ◽  
Churn Prediction ◽  
Important Data ◽  
Data Set ◽  
Content Type ◽  
Customer Churn

Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of this paper, performing these data preprocessing tasks, is to make the mining algorithm produce good quality mining results. Design/methodology/approach – Based on a real telecom customer churn data set, seven different preprocessed data sets based on performing feature selection and data reduction by different priorities are used to train the artificial neural network as the churn prediction model. Findings – The results show that performing data reduction first by self-organizing maps and feature selection second by principal component analysis can allow the prediction model to provide the highest prediction accuracy. In addition, this priority allows the prediction model for more efficient learning since 66 and 62 percent of the original features and data samples are reduced, respectively. Originality/value – The contribution of this paper is to understand the better procedure of performing the two important data preprocessing steps for telecom churn prediction.

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