Literature Review on Data Mining

Literature Review on Data Mining

Accounting BISA HBS

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Contents

1. Introduction

2. Data mining techniques applied in diverse industry

2.1 Machine learning

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2.1.1 Classification

2.1.2 Prediction

2.1.3 Association

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2.1.4 Others

2.2 Clustering analysis

3 Sentiment analysis

3.1 Semantic orientation approach

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3.2 Machine learning approach

3.3 Comparison and Combination of both methods

4 Textual analysis

5 Future research direction

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6 Conclusion

Reference

1. Introduction

Big data is emerging as a quite popular theme among practitioners and scholars. For examples, many companies use digital technologies to track social media on a real-time basis, thereby creating longitudinal structures of millions of posts, tweets, or reviews George et al. (2014). Contemporaneously, a large number of research papers are dedicated on the revolutionary of big data in different industry and employ 3Vs to describe big data, the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed(McAfee and Brynjolfsson, 2012). Inevitably, people from very walks of life nowadays are living in the era of big data.

In 2014, Bank of America appointed a new advanced position, the Chief Analytics Officer(CAO), to Douglas Hague who is the former senior vice president of vendor analytics(Ferguson, 2014). He strongly expressed a recognition of the importance of analytics in the use of big data, rather than just reporting on data(Ferguson, 2014). Precisely, companies need to learn taking analytics beyond the data and information which could come into the real insights that impact decision making. Sanders (2016 p.28) exactly gave the difference between big data and analytics, “Big data without analytics is just a massive amount of data. Analytics without big data are simply mathematical and statistical tools and applications”. Hence, oriented by big data, we should put more efforts on big data analytics(BDA), which could be defined as “ a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high velocity capture, discovery and /or analysis.” (Carter, 2011).

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Along with the technology of big data analytics being mature gradually, the concept of business intelligence arose through the whole economic globe. Enterprises tend to explore all kinds of deeply hidden features from mass information networks, with prevalent and intelligent analytics skills such as data mining, process mining, web mining or text mining. Business corporations aims to make predictive decision or take action towards a targeted goal with help of these mined valuable information. Social media is one good example of this. Marketing seems the most direct beneficial field of social media analysis. There have been a number of literature involving the role of social media in marketing and the relationship between market variables and online users generated behaviors (Chen et al., 2011). Likewise, Senadheera et al. (2017) affirmed that social media could act as an information system which encourage enterprises to adopt more business applications via social media platforms, for example interact with lower cost with their customer for feedback.

In the accounting and finance field, one emerging research direction is to explore the content, tone or sentiment of social media information. The focuses on the link between these new type financial information and financial markets are becoming the prevalent trends. For example, research work from Yu et al. (2013) suggested that the overall social media had a strong relationship with firm stock performance with stock returns and risks as indicators. More innovatively, Purda and Skillicorn (2015) employed the latest data mining techniques to detect the fraudulent activity of Management Discussion and Analysis (MD&A) sections of annual report. Different with conventional financial media, such as news or press release, user generated content from social media is unstructured and real-time updated with mass volume. Hence a new kind of methodology combined with big data analytics and statistics need to be concerned in order to help researchers get developing insights about the relationship between social media and financial markets. According to Miller and Skinner (2015), there is still relatively little research work on how firms use social media for disseminating or disclosing financial information. And little is known about how to use the latest big data analytics to explore the hidden financial information from social media. Thus our research attempt to employ the data mining techniques well-developed in other industry like computer science or marketing to get more insights on financial disclosure on social media.

This literature review will focus on data mining techniques and aim to obtain more understanding on the features, benefits, and context of data mining techniques to be applied on, in order to employ the appropriate and powerful big data analytics for accounting research, especially for financial disclosure, under the circumstance of corporation financial disclosure on social media platform. The paper will be organized as the following structures. In section 2, details about different data mining techniques will be discussed. The literature which applied specific techniques from diverse industries will be listed and compared. In section 3&4, more specific data mining application domain will be reviewed, for example, data mining could be used to improve sentiment analysis and text analysis which is our key focus on naturally language analytics, since this is most related to financial disclosure research. In section 5 we discuss future research direction and in section 6 we conclude the whole paper.

2. Data mining techniques applied in diverse industry

Continuous innovation of computational technology created diverse categories of data mining techniques. This literature review will focus on several popular and widely used data mining techniques in academic papers. To help categorize the literature, Table 1 and Table 2 tabulate most of the papers that I will discuss in this section based on the employed data mining techniques and their explored industry topic. Two main data mining categories will be reviewed as follow as machine learning and clustering analysis.

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1.         Machine learning

Machine learning is probably the better known and most familiar technique in the field of data mining. It originated from computer gaming and artificial intelligence that give the computer the ability to learn without being explicitly programmed(Samuel, 2000). Machine learning tasks are generally classified into two situations, supervised learning and unsupervised learning. Supervised learning involved training computer with presented examples. The goal is to learn the rules from the training set and then apply them into new test task. Unsupervised learning techniques do not require labeled data leaving its own to explore patterns or structures. A very good example to illustrate this is AlphaGo Zero which is an artificial-intelligence chess program from Google DeepMind team. It mastered the chess game without any human data or guidance, which is strongly beating the previous version which did require a large training database to learn(Singh et al., 2017).

Machine learning system could realize different function, such as classification, prediction and explore association. Different data mining techniques or algorithms (i.e support vector machine, decision tree) could be employed in working systems to achieve the expected function with evaluation focusing on accuracy rate(Bandaru et al., 2017).Table 1 lists the sample papers on how to use different tools to build data mining system.

1. Classification

Classification models could predict categorical class labels. For example, Abdelhamid et al. (2014) dealt with website phishing as a typical classification problem in which the goal is to assign a test data into phishy, legitimate, or suspicious, etc. The working of classification includes two steps, building classifier using classification algorithms based on learning from the training set, then using classifier for classification. This is typically considered as supervised classification methods and Traore et al. (2017) used this method to process a set of satellite images, classifying new areas into epidemic risk or not an epidemic risk in order to find the link between the geographical area of the Niger River and the spread of the cholera epidemic.

Kim and Lee (2014) modelled SQLIA detection as a data-mining based binary classification problem utilizing the support vector machine which is beneficial in detecting unknown attacks with high accuracy. Support vector machines(SVM) emerging in the nineties have become one of the most widespread machine learning techniques(Moro et al., 2016).It transforms the input into a high m-dimensional vector space then utilizes the algorithm to build the classifier finding the best linear separating hyper plane(Moro et al., 2016). Latkowski and Osowski (2015) combined SVM with other algorithms in an ensemble to select the most significant genes in the expression microarray of autism.

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