Data tools: Analysis of big data involving text mining

The demand for big data talent is growing, and there is a shortage of data analytics talent in United States. Because data analytics is used by many different industries and the data analytics is an interdisciplinary sector, learning and teaching it requires careful planning. This post discusses how big data analytics can be implemented in a given case study.

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Definitions

Big data – any set of data that has high velocity, volume, and variety, also known as the 3Vs (Davenport & Dyche, 2013; Fox & Do 2013, Podesta, Pritzker, Moniz, Holdren, & Zients, 2014).

Text mining – a process that involves discovering implicit knowledge from unstructured textual data (Gera & Goel, 2015; Hashimi & Hafez, 2015; Nassirtoussi Aghabozorgi, Wah, & Ngo, 2015).

Case study: Basole, Seuss, and Rouse (2013). IT innovation adoption by enterprises: Knowledge discovery through text analytics.

The goal of this study was to use text mining techniques on 472 quality peer reviewed articles that spanned 30 years of knowledge (1977-2008).  The selection criteria for the articles were based on articles focused on the adoption of IT innovation; focused on the enterprise, organization, or firm; rigorous research methods; and publishable leading journals.  The reason to go through all this analysis is to prove the usefulness of text analytics for literature reviews.  In 2016, most literature reviews contain recent literature from the last five years, and in certain fields, it may not just be useful to focus on the last five years.  Extending the literature search beyond this 5-year period, requires a ton of attention and manual labor, which makes the already literature an even more time-consuming endeavor than before. So, the author’s question is to see if it is possible to use text mining to conduct a more thorough review of the body of knowledge that expands beyond just the typical five years on any subject matter.  They argue that the time it takes to conduct this tedious task could benefit from automation.  However, this should be thought of as a first pass through the literature review. Thinking of this regarding a first pass allows for the generation of new research questions and a generation of ideas, which drives more future analysis.In the end, the study was able to conclude that cost and complexity were two of the most frequent determinants of IT innovation adoption from the perspective of an IT department.  Other determinants for IT departments were the complexity, capability, and relative advantage of the innovation.  However, when going up one level of extraction to the enterprise/organizational level, the perceived benefits and usefulness were the main determinants of IT innovation.  Ease of use of the technology was a big deal for the organization.  When comparing, IT innovation with costs there was a negative correlation between the two, while IT innovation has a positive correlation to organization size and top management support.

How was big data analytics learned, taught, and used in the case study?

The research approach for this study was: (1) Document Identification and extraction, (2) document classification and coding, (3) document analysis and knowledge discovery (key terms, co-occurrence), and (4) research gap identification.

Analysis of the data consisted of classifying the data into four time periods (bins): 1988-1979; 1980-1989; 1990-1999; and 2000-2008 and use of a classification scheme based on existing taxonomies (case study, content analysis, field experiment, field study, frameworks and conceptual model, interview, laboratory experiment, literature analysis, mathematical model, qualitative research, secondary data, speculation/commentary, and survey).  Data was also classified by their functional discipline (Information systems and computer science, decision science, management and organization sciences, economics, and innovation) and finally by IT innovation (software, hardware, networking infrastructure, and the tool’s IT term catalog). This study used a tool called Northernlight (http://georgiatech.northernlight.com/).

The hopes of this study are to use the bag-of-words technique and word proximity to other words (or their equivalents) to help extract meaning from a large set of text-based documents.  Bag-of-words technique is known for counting and identifying key terms and phrases, which help uncover themes.  The simplest way of thinking of the bag-of-words technique is word frequencies in a document.

However, understanding the meaning behind the themes means studying the context in which the words are located in, and relating them amongst other themes, also called co-occurrence of terms.  The best way of doing this meaning extraction is to measure the strength/distance between the themes.  Finally, the researcher in this study can set minimums, maximums that can enhance the meaning extraction algorithm to garner insights into IT innovation, while reducing the overall noise in the final results. The researchers set the following rules for co-occurrences between themes:

  • There are approximately 40 words per sentence
  • There are approximately 150 words per paragraph

How could this implementation of big data have been improved upon?

Goldbloom (2016) stated that using big data techniques (machine learning) is best on big data that requires classifying and it breaks down when the task is too small and specialized, therefore prime for only human analysis.  This study only looked at 427 articles, is this considered big enough for analysis, or should the analysis go back through multiple years beyond just the 30 years (Basole et al., 2013).  What is considered big data in 2013 (the time of this study), may not be big data in 2023 (Fox & Do, 2013).

Mei & Zhai (2005), observed how terms and term frequencies evolved over time and graphed it by year, rather than binning the data into four different groups as in Basole et al. (2013).  This case study could have shown how cost and complexity in IT innovation changed over time.  Graphing the results similar to Mei & Zhai (2005) and Yoon and Song (2014) would also allow for an analysis of IT innovation themes and if each of these themes is in an Introduction, Growth, Majority, or Decline mode.

 Reference

  • Basole, R. C., Seuss, C. D., & Rouse, W. B. (2013). IT innovation adoption by enterprises: Knowledge discovery through text analytics. Decision Support Systems, 54, 1044-1054. Retrieved from http://www.sciencedirect.com.ctu.idm.oclc.org/science/article/pii/S0167923612002849
  • Davenport, T. H., & Dyche, J. (2013). Big Data in Big Companies. International Institute for Analytics, (May), 1–31.
  • Fox, S., & Do, T. (2013). Getting real about Big Data: applying critical realism to analyse Big Data hype. International Journal of Managing Projects in Business, 6(4), 739–760. http://doi.org/10.1108/IJMPB-08-2012-0049
  • Gera, M., & Goel, S. (2015). Data Mining-Techniques, Methods and Algorithms: A Review on Tools and their Validity. International Journal of Computer Applications, 113(18), 22–29.
  • Goldbloom, A. (2016). The jobs we’ll lose to machines –and the ones we won’t. TED. Retrieved from http://www.ted.com/talks/anthony_goldbloom_the_jobs_we_ll_lose_to_machines_and_the_ones_we_won_t
  • Hashimi, H., & Hafez, A. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51, 729–733. http://doi.org/10.1016/j.chb.2014.10.062
  • Mei, Q., & Zhai, C. (2005). Discovering evolutionary theme patterns from text: an exploration of temporal text mining. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 198–207. http://doi.org/10.1145/1081870.1081895
  • Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2015). Text-mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Systems with Applications42(1), 306-324.
  • Podesta, J., Pritzker, P., Moniz, E. J., Holdren, J., & Zients, J. (2014). Big Data: Seizing Opportunities. Executive Office of the President of USA, 1–79.
  • Yoon, B., & Song, B. (2014). A systematic approach of partner selection for open innovation. Industrial Management & Data Systems, 114(7), 1068.

Data Tools: WEKA

Many tools are used for the purpose of data analytics. WEKA is one of those free tools in the market.

WEKA

The Java based, open sourced, and platform independent Waikato Environment for Knowledge Analysis (WEKA) tool, for data preprocessing, predictive data analytics, and facilitation interpretations and evaluation (Dogan & Tanrikulu, 2013; Gera & Goel, 2015; Miranda, n.d.; Xia & Gong, 2014).  It was originally developed for analyzing agricultural data and has evolved to house a comprehensive collection of data preprocessing and modeling techniques (Patel & Donga 2015).  It is a java based machine learning algorithm for data mining tasks as well as text mining that could be used for predictive modeling, housing pre-processing, classification, regression, clustering, association rules, and visualization (WEKA, n.d). Also, WEKA contains classification, clustering, association rules, regression, and visualization capabilities, in particular, the C4.5 decision tree predictive data analytics algorithm (Dogan & Tanrikulu, 2013; Gera & Goel, 2015; Hachey & Grover, 2006; Kumar & Fet, 2011). Here WEKA is an open source data and text mining software tool, thus it is free to use. Therefore there are no costs associated with this software solution.

WEKA can be applied to big data (WEKA, n.d.) and SQL Databases (Patel & Donga, 2015). Subsequently, WEKA has been used in many research studies that are involved in big data analytics (Dogan & Tanrikulu, 2013; Gera & Goel, 2015; Hachey & Grover, 2006; Kumar & Fet, 2011; Parkavi & Sasikumar, 2016; Xia & Gong, 2014). For instance, Barak and Modarres (2015) used WEKA for decision tree analysis on predicting stock risks and returns.

The fact that it has been using in this many research studies is that the reliability and validity of the software are high and well established.  Even in a study comparing WEKA with 12 other data analytics tools, is one of two apps studied that have a classification, regression, and clustering algorithms (Gera & Goel, 2015).

A disadvantage of using this tool is its lack of supporting multi-relational data mining, but if one can link all the multi-relational data into one table, it can do its job (Patel & Donga, 2015). The comprehensiveness of analysis algorithms for both data and text mining and pre-processing is its advantage. Another disadvantage of WEKA is that it cannot handle raw data directly, meaning the data had to be preprocessed before it is entered into the software package and analyzed (Hoonlor, 2011). WEKA cannot even import excel files, data in Excel have to be converted into CSV format to be usable within the system (Miranda, n.d.)

References:

  • Dogan, N., & Tanrikulu, Z. (2013). A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness. Information Technology and Management, 14(2), 105-124. doi:http://dx.doi.org/10.1007/s10799-012-0135-7
  • Gera, M., & Goel, S. (2015). Data Mining -Techniques, Methods and Algorithms: A Review on Tools and their Validity. International Journal of Computer Applications, 113(18), 22–29.
  • Hoonlor, A. (2011). Sequential patterns and temporal patterns for text mining. UMI Dissertation Publishing.
  • Kumar, D., & Fet, D. (2011). Performance Analysis of Various Data Mining Algorithms: A Review. International Journal of Computer Applications, 32(6), 9–16.
  • Miranda, S. (n.d.). An Introduction to Social Analytics : Concepts and Methods.
  • Parkavi, S. & Sasikumar, S. (2016). Prediction of Commodities Market by Using Data Mining Technique. i-Manager’s Journal on Computer Science.
  • Patel, K., & Donga, J. (2015). Practical Approaches: A Survey on Data Mining Practical Tools. Foundations, 2(9).
  • WEKA (n.d.) WEKA 3: Data Mining Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ml/weka/
  • Xia, B. S., & Gong, P. (2014). Review of business intelligence through data analysis. Benchmarking, 21(2), 300–311. http://doi.org/http://dx.doi.org/10.1108/BIJ-08-2012-0051

Big Data Analytics: R

R has proven to be the most effective software tool for analyzing big data. This post will be quick discuss of my evaluation of this tool and its relevance in the big data arena, with respects to text mining.

R is a powerful statistical tool that can aid in data mining.  Thus, it has huge relevance in the big data arena.  Focusing on my project, I have found that R has a text mining package [tm()].

Patal and Donga (2015) and Fayyad, Piatetsky-Shapiro, & Smyth, (1996) say that the main techniques in Data Mining are: anomaly detection (outlier/change/deviation detection), association rule learning (relationships between the variables), clustering (grouping data that are similar to another), classification (taking a known structure to new data), regressions (find a function to describe the data), and summarization (visualizations, reports, dashboards). Whereas, According to Ghosh, Roy, & Bandyopadhyay (2012), the main types of Text Mining techniques are: text categorization (assign text/documents with pre-defined categories), text-clustering (group similar text/documents together), concept mining (discovering concept/logic based ideas), Information retrieval (finding the relevant documents per the query), and information extraction (id key phrases and relationships within the text). Meanwhile, Agrawal and Batra (2013) add: summarization (compressed representation of the input), assessing document similarity (similarities between different documents), document retrieval (id and grabbing the most relevant documents), to the list of text mining techniques.

We use the “library(tm)” to aid in transforming text, stem words, build a term-document matrix, etc. mostly for preprocessing the data (RStudio pubs, n.d.). Based on RStudio pubs (n.d.) some text preprocessing steps and code are as follows:

  • To remove punctuation:

docs <- tm_map(docs, removePunctuation)

  • To remove special characters:

for(j in seq(docs))      {        docs[[j]] <- gsub(“/”, ” “, docs[[j]])        docs[[j]] <- gsub(“@”, ” “, docs[[j]])        docs[[j]] <- gsub(“\\|”, ” “, docs[[j]])     }

  • To remove numbers:

docs <- tm_map(docs, removeNumbers)

  • Convert to lowercase:

docs <- tm_map(docs, tolower)

  • Removing “stopwords”/common words

docs <- tm_map(docs, removeWords, stopwords(“english”))

  • Removing particular words

docs <- tm_map(docs, removeWords, c(“department”, “email”))

  • Combining words that should stay together

for (j in seq(docs)){docs[[j]] <- gsub(“qualitative research”, “QDA”, docs[[j]])docs[[j]] <- gsub(“qualitative studies”, “QDA”, docs[[j]])docs[[j]] <- gsub(“qualitative analysis”, “QDA”, docs[[j]])docs[[j]] <- gsub(“research methods”, “research_methods”, docs[[j]])}

  • Removing coming word endings

library(SnowballC)   docs <- tm_map(docs, stemDocument)

Text mining algorithms could consist of but are not limited to (Zhao, 2013):

  • Summarization:
    • Word clouds use “library (wordcloud)”
    • Word frequencies
  • Regressions
    • Term correlations use “library (ggplot2) use functions findAssocs”
    • Plot word frequencies Term correlations use “library (ggplot2)”
  • Classification models:
    • Decision Tree “library (party)” or “library (rpart)”
  • Association models:
    • Apriori use “library (arules)”
  • Clustering models:
    • K-mean clustering use “library (fpc)”
    • K-medoids clustering use “library(fpc)”
    • Hierarchical clustering use “library(cluster)”
    • Density-based clustering use “library (fpc)”

As we can see, there are current libraries, functions, etc. to help with data preprocessing, data mining, and data visualization when it comes to text mining with R and RStudio.

Resources:

Big Data Analytics: Pizza Industry

The ability to evaluate structured and unstructured data will provide an organization with a competitive edge. This post would explain just one example of how big data analytics can be used by these organizations to gain a competitive advantage.

Pizza, pizza! A competitive analysis was completed on Dominos, Pizza Hut, and Papa Johns.  Competitive analysis is gathering external data that is available freely, i.e. social media like Twitter tweets and Facebook posts.  That is what He, Zha, and Li (2013) studied, approximately 307 total tweets (266 from Dominos, 24 from Papa John, 17 from Pizza Hut) and 135 wall post (63 from Dominos, 37 from Papa Johns, 35 from Pizza Hut), for the month October 2011(He et al, 2013).  It should be noted that these are the big three pizza chain controlling 23% of the total market share (7.6% from Dominos, 4.23% from Papa Johns, 11.65% from Pizza Hut)(He et al., 2013) (He et al., 2013). Posts and tweets contain text data, videos, and pictures.  All the data collected was text-based data and collected manually, and SPSS Clementine tool was used to discover themes in their text (He et al., 2013).

He et al. (2013), found that Domino’s Pizza was using social media to engage their customers the most.  Domino’s Pizza did the most to reply to as many tweets and posts.  The types of posts in all three companies varied from the promotion to marketing to polling (i.e. “What is your favorite topping?”), facts about pizza, Halloween-themed posts, baseball themed posts, etc. (He et al., 2013).  Results from the text mining of all three companies: Ordering and delivery was key (customers shared the experience and feelings about their experience), Pizza Quality (taste & quality), Feedback on customers’ purchase decisions, Casual socialization posts (i.e. Happy Halloween, Happy Friday), and Marketing tweets (posts on current deals, promotions and advertisement) (He et al, 2013).  Besides text mining, there was also content analysis on each of their sites (367 pictures & 67 videos from Dominos, 196 pictures & 40 videos from Papa Johns, and 106 pictures and 42 videos from Pizza Hut), which showed that the big three were trying to drive customer engagement (He et al., 2013).

He et al. (2013) lists the theory that with higher positive customer engagement, customers can become brand advocates, which increases their brand loyalty and push referrals to their friends, and approximately 1/3 people followed a friend’s referral if done through social media.  Thus, evaluating the structure and unstructured data provided to an organization about their own product and theirs of their competitors, they could use it to help increase their customer services, driving improvements in their own products, and driving more customers to their products (He et al., 2013).  Key lessons from this study, which would help any organization gain an advantage in the market are to (1) Constantly monitor your social media and those of your competitors, (2) Establish a benchmark of how many posts, likes, shares, etc. between you and your competitors, (3) Mine the conversational data for content and context, and (4) analyze the impact of your social media footprint to your own business (when prices rise or fall what is the response, etc.) (He et al, 2013).

Resources:

  • He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472.