Textual data mining for industrial knowledge management and text classification: A business oriented approach

Ur-Rahman, Nadeem; Harding, Jennifer

doi:10.1016/j.eswa.2011.09.124

Cited by 123 publications

(74 citation statements)

References 23 publications

(25 reference statements)

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“…They analyzed the customers of a large German business-to-business mail-order company. Ur-Rahman and Harding [23] focused on the use of hybrid applications of TM or textual DM techniques to classify textual data into two different classes. Hao [24] compared the k-medoids algorithm and k-medoids social evolutionary programming in clustering documents.…”

Section: Related Researchmentioning

confidence: 99%

Mining Customer Feedback Documents

Stoica¹,

Özyirmidokuz²

2015

IJKE

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Abstract-Managing customer feedback data has become a necessity for firms in order for them to gain competitive advantage in the sector. Analyzing customer complaints' data to find useful information that's hidden is an important step in understanding customers. This important, hidden knowledge must be extracted automatically to allow firms to gain a better understanding of the general market and of their own and their competitors' customers. A firm can learn the needs of customers and show how its products and services satisfy these needs by analyzing these documents.The aim of this research is to summarize and extract data from unstructured customer feedback documents which are about ignoring subscriptions to a telecommunication firm in Turkey. The data are transformed to a collection of documents by generating a document for each record. Text processing techniques are applied. Cosine similarity analysis is used to classify documents into relevant categories. Clusters are determined.

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Section: Related Researchmentioning

confidence: 99%

Mining Customer Feedback Documents

Stoica¹,

Özyirmidokuz²

2015

IJKE

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“…In the previous research, Naïve Bayesian (Kang et al 2012;Yoshida et al 2014), support vector machine (Mullen and Collier 2004) and decision tree (Sui et al 2003) have been used for implementation of sentiment classification and these algorithms are reported to be effective for sentiment classification in the literature (Forman 2003;Dhillon et al 2003;Sebastiani 2005;Wan et al 2012;Ur-Rahman and Harding 2012). In this study, we also use these three algorithm for the experiment of impact of data properties on sentiment classification performance.…”

Section: Machine Learning Approach (Mla)mentioning

confidence: 99%

Data properties and the performance of sentiment classification for electronic commerce applications

Choi

Lee

2017

Inf Syst Front

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Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.

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“…The sentiment analysis can be categorized into two different ways: linguistic and nonlinguistic [7,35]. Linguistic techniques consider the document as the set of natural languages and structure such as syntax and grammar, whereas nonlinguistic techniques regard documents as a series of characters [36]. Therefore, linguistic techniques measure the document sentiment based on the word dictionaries such as WordNet, whereas non-linguistic techniques do based on the word proximity to other related terms in the document or in related documents [7,37].…”

Section: Sentiment Analysis For Analyzing Customer Reviewsmentioning

confidence: 99%

Analyzing Dynamic Change in Customer Requirements: An Approach Using Review-Based Kano Analysis

2018

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Abstract:To seek sustainable product development, understanding customer requirements is critically important where the life cycle of products or services is so fast, and continuous updates should be provided. In particular, how a customer feels for the specific function of the product/service and how their needs have changed is a critical question. According to Kano model dynamics, customer requirements for certain functions change over time, because customers firstly feel attracted to the new service characteristics but come to take them for granted over time. However, previous research on proving this theory has relied on customer surveys and interviews, which are highly time-consuming and expensive. In response, this study suggests customer review-based analysis to investigate Kano model dynamics, because customer reviews can be considered to be excellent sources for reflecting customer needs. This study firstly categorizes customer reviews into two types-positive reviews and supplementation-required reviews-and suggests a five-section framework according to the frequency of each review type. We define characteristics of each section from the perspective of the Kano model. Based on this framework, we analyze the dynamics of customer requirements in the online businesses, for which customer reviews are the main indicator of service quality.

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Textual data mining for industrial knowledge management and text classification: A business oriented approach

Abstract: Citation: UR-RAHMAN, N. and HARDING, J.A., 2012

Cited by 123 publications

References 23 publications

Mining Customer Feedback Documents

Mining Customer Feedback Documents

Data properties and the performance of sentiment classification for electronic commerce applications

Analyzing Dynamic Change in Customer Requirements: An Approach Using Review-Based Kano Analysis

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