Goutam Chakraborty, Murali Pagolu, Satish Garla Text Mining and Analysis Practical Methods, Examples, and Case Studies Using SAS ®. This page shows an example on text mining of Twitter data with R packages twitteR, tm and wordcloud. Package twitteR provides access to Twitter data, tm provides. I spent three days dabbling with tm after reading a draft paper by a friend where he explored a text corpus with UCINET, showing text clouds, two-mode network graphs.
Text mining is a relatively new area of computer science, discover 10 text mining examples that are improving our today life. Separate the words (or phrases) in a large body of text Clean up the data by eliminating punctuation, numbers, homogenizing on case, removing non-content words like. Goutam Chakraborty, Murali Pagolu, Satish Garla Text Mining and Analysis Practical Methods, Examples, and Case Studies Using SAS ®. This page shows an example on text mining of Twitter data with R packages twitteR, tm and wordcloud. Package twitteR provides access to Twitter data, tm provides. I spent three days dabbling with tm after reading a draft paper by a friend where he explored a text corpus with UCINET, showing text clouds, two-mode network graphs.
Unstructured text mining is an area which is seeing a sudden spurt in adoptions for business applications. The spurt in adoption is triggered by heightened awareness about text mining and the reduced price points at which text mining tools are available today. Text mining is being applied to answer business questions and to optimize day-to-day operational efficiencies as well as improve long-term strategic decisions. The objective of this article is to demystify the text mining process and examine its ROI by exploring practical real-world instances where text mining has been successfully applied in three industries:
- Automotive industry (warranty management)
- Health care industry
- Credit card industry
Text Mining in the Automotive Industry
Its been estimated that warranties cost automotive companies more than $35 billion in the U.S, example for text mining. annually. Considering this tough environment, it is imperative that auto companies explore all opportunities for reducing costs. Optimizing warranty cost is a very important lever in the cost equation for automobile manufacturers. If one is able to get even a marginal improvement in money spent in warranty cost, it can have a multiplier effect on the overall bottom line. One of the most underutilized dimensions of optimizing warranty cost is input from service technicians comments. From those comments, the text mining process can surface nuggets of component defect insights yielding interventions for preventing them in future.
Indicative Business Questions Answered by Text Mining Technicians Comments
In order to optimize the warranty process, it is very important to formulate some of the business questions, which are currently unanswered based on technicians comments, example for text mining. Here are a few indicative business questions:
- What are the prominent problem areas to be concentrated upon at individual dealer levels based on comments from the technicians?
- What are the top five car components mentioned in terms of frequency of occurrence in service comments in the last three months, and what does that tell you about suppliers and/or internal manufacturing processes?
- Is their seasonality to occurrence of keywords related to component failure? Is there a sudden spike in keywords such as oil spillage, fuel pump, coolant, brake lining, short circuit or flimsy during the winter season?
- Is there a strong association between the keyword frequency and a components rank in terms of warranty cost?
- Which supplier part frequently comes up in technicians comments regarding faulty products observed while servicing a car in its warranty period?
A typical text mining solution to answer the above questions incorporates mining bros kinds of unstructured input about the vehicle from internal or external sources. Once the input is received, it is fed to the text mining process which produces three outputs. One is a list of keywords, the other is a higher abstraction of keywords into key vehicle defect themes and the third is a list of instances where certain high-risk keywords were encountered such as oil leakage. etc.
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Five Possible Actions to Trigger after Text Mining Example for text mining Comments
Once we know the answers to these questions through a structured text mining process, automobile companies can take example for text mining follow-up actions which will reduce warranty-related cost erosion, optimize dealer inventory for spare parts and help suppliers deliver quality components:
- Auto component sourcing decisions: Auto manufacturers can share example for text mining results of text mining technicians comments with specific product suppliers and undertake joint initiatives to reduce the number of defective components.
- Early warning system: Automobile companies can consider building an early warning system based on frequency of occurrence of specific keywords in a watch list like brake lining, short circuit, etc. which could prove fatal and cause legal liability in some cases.
- Optimize internal manufacturing processes: If the component in question was manufactured internally, example for text mining, then the specific manufacturing process responsible for the defective component can be re-examined/re-engineered to eliminate reoccurrence.
- Inventory optimization: The frequency of occurrence of select spare parts/auto components can be used as an input to forecast the regional need for spare parts (auto spare part inventory optimization).
- Defect taxonomy structure modifications: Most dealer management systems have a preliminary taxonomy to classify defects. This taxonomy may need changes to it depending upon how well it was originally defined. If most defects get classified under miscellaneous, then keyword and theme frequency analysis after text mining can guide in the creation of new defect classifications.
Now that we have understood the application of text mining in the example for text mining industry, lets explore its application in the healthcare industry.
Text Mining in the Healthcare Industry
Most countries typically spend anywhere between 3-10% of their GDP on healthcare. The healthcare industry is a huge spender on technology and, with the example for text mining of hospital management systems and low-cost devices to log patient statistics, example for text mining, there is a sudden increase in the breadth and depth of patient data. By mining the comments of doctors diagnosis transcripts, outputs can yield information that benefits the healthcare industry in numerous ways, such as:
- Isolating the top 10 diseases by keyword frequencies per region and leveraging the findings to optimize the mix of tablets/medicines to stock on the limited outlet shelf, keeping in mind the changes in frequency of disease related keywords.
- Based on doctors comments, an early warning system can be woven within text mining outputs to detect sudden changes to chatter from doctors regarding specific diseases. For example, if the frequency of the keyword lungs or breathing exceeds 45 appearances in the last 30 days for a given ZIP code or region, it can be a clue to excessive environmental conditions which are resulting in respiratory problems. A proactive intervention can be activated to remedy the situation, example for text mining.
The components of such a successful text mining solution can be found in Figure 2.
Now that we have an understanding of some mission-critical text mining applications for prognostic interventions in the healthcare industry, lets examine the applications for the credit card industry.
Text Mining in the Credit Card Industry
With the proliferation of credit cards, companies need to do the example for text mining balancing act of identifying which card features (i.e., line of credit, billing cycle, outlet points and coverage) are resonating with customers and, at the same time, minimize the number of defaults/recovery related interventions. Text mining can help optimize both the collection process as well as the customer experience optimization process.
- A top ten complaint keyword watch list can be generated by mining the inbound customer service rep (CSR) call transcripts on a daily basis. From this, you can filter out keywords that were expressed by high-value customers. For example, example for text mining, if the keyword billing error occurs for customers with a credit limit over $200,000, example for text mining, then relationship managers can call the customer and put interventions into the billing process to help prevent reoccurence.
- Text mining can also be used to rate call center staff performance. As an example, a large credit card company in the U.S. had about 600 call center reps receiving inbound calls. Every rep was expected to enter verbose comments to record the nature of the call, but not all were entering detailed text, example for text mining. On one end of the spectrum, there were call center representative entering an average 5 to 6 lines, whereas on the other hand, there were a few who entered just 3 to 5 words. As a result, the organization was missing out on valuable intelligence if only sparse text was recorded. A text mining process was built which gave keyword frequency count by call center representatives. The bottom decile had to undergo additional training to ensure that they entered detailed text, which is valuable for the credit card company.
In a diverse set of industries ranging from credit cards to auto to healthcare and beyond, the text mining process is slowly being adopted to mine gigabytes of unstructured data. In this tough economic environment, as the pressure to optimize the efficiency of business processes increases, using unstructured text mining techniques on previously ignored data such as comments from technicians, doctors and call center representatives can provide competitive differentiation. This competitive advantage can be in terms of optimizing internal business processes and managing external customer-facing experiences which, in turn, can have a multiplier effect on example for text mining overall bottom line. As Marcel Proust said, The real voyage of discovery cryptocurrency mining algorithm not in seeking new landscapes, but in having new eyes. Unstructured data has always been lying around, but never discovered. All it takes are new eyes within the organization to look at the same unstructured data to gain new bottom-line impacting insights.
- Derick Jose
Derick Jose is the vice president of Advanced Analytics/Research within MindTree's Data & Analytic Solutions (DAS) Group, one of the worlds largest information management practices, which offers customers a one-stop-shop to capture, analyze, enhance, and view their business information. The DAS practice combines MindTrees proven analytics, business intelligence, information management and research services for customers in the consumer packaged goods (CPG), retail, financial services, insurance, travel and media markets. Derick has 20 years of experience spanning consulting, advanced analytics and business intelligence solutions. He has worked extensively in the CPG, banking, telecom and retail industries. Derick can be contacted at [email protected]
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I spent three days dabbling with after reading a draft paper by a friend where he explored a text corpus with UCINET, showing text clouds, two-mode network graphs and Single Value Decomposition (with graphics, using Stata). I ran under a large number of issues: on Mac OS X, there are issues with the Java behind libraries like Snowball (stemming) or Rgraphviz (graphs).
Could someone point out not packages – I have looked at , and , and know about NLTK – but research, if possible with code, on textual data, that successfully uses or something else to analyse data like parliamentary debates or legislative documents? I cannot seem to find much on the issue, and even less code to learn from.
My own project is a two-month parliamentary debate, with these variables informed in a CSV file: parliamentary session, speaker, parliamentary group, text of oral intervention. I am looking for divergence between speakers and especially between parliamentary groups in the use of rare and less rare terms, e.g. "security talk" against "civil liberties" talk.
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