Text and data mining Text and data mining Find a better way to download, search, filter and understand millions of articles and books published on ScienceDirect. Text mining is widely used in the industry when data is unstructured. Derived information can be provided in the form of numbers (indices), categories or clusters. Feb 22, 2010 · Three Real-World Applications of Text Mining to Solve Specific Business Problems Analytics Derick Text mining can also be used to rate call center.
Text mining provides functionality that can compare documents by examining the terms used within them. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as . Text and data mining Text and data mining Find a better way to download, search, filter and understand millions of articles and books published on ScienceDirect. Text mining is widely used in the industry when data is unstructured. Derived information can be provided in the form of numbers (indices), categories or clusters. Feb 22, 2010 · Three Real-World Applications of Text Mining to Solve Specific Business Problems Analytics Derick Text mining can also be used to rate call center.
I have worked on building a fraud detection solution using text mining, so I understand the scenario that has led to this question. I'll talk about the approach/techniques that is to be followed to build the fraud detection solution.I'll divide it into 4 sections
- Build line of business fraud dictionary
- Fraud detection & scoring
- Additional what is text mining used for Fraud Dictionary:
As a first step you'll have to identify fraud concepts with help from subject matter expert for the line of business in concern. Fraud concept is a person, characteristic, entity, or event that represents a suspicious scenario and similar concepts can be grouped together to form a concept category. Each concept is further represented by words, phrases, entities etc. The presence of these words/phrases in the document implies occurrence of fraud concept in that transaction. The end result of this exercise will be a fraud dictionary that is a repository of concepts and suspicious key words.
For example considering the data set mentioned in the question, the become the concept category, and and are 2 different concepts. There will be a keywords/phrases that is associated to this concept which should be captured onto the fraud dictionary.
You can use NLP technique to build the dictionary.
Fraud detection & scoring:
Phrases/Keywords to be semantically matched with Phrases/Keywords from Fraud Dictionary and Fraud concepts in transactions can be identified. At this point, care needs to be taken to maintain the context (negation, positive sense) in which a keyword/phrase has been used to avoid false positives.
For each identified occurrence of Fraud concept in the transaction, a weight of 1 can be assigned. This is done for calculating a suspicious score for the transaction. Higher the score high suspecious.
Note that using a combination of larger data set with high textual content, and extensive fraud dictionary would result in higher number potentially identified.
You can consider using OpenNLP / StanfordNLP for Part of Speech tagging. Most of the programming language have supporting library for OpenNLP/StanfordNLP. You can choose the language based on your comfort.
You can refer here to get an idea of extracting concepts from sentences
Fruther read my blog on Text mining 101 to learn more about
- TM process overview
- Calculate term weight (TF-IDF)
- Similarity distance measure (Cosine)
- Overview of key text mining techniques
Hope this helps.
1. How did BBVA use text mining? | Ideal Term PapersFeb 22, 2010 · Three Real-World Applications of Text Mining to Solve Specific Business Problems Analytics Derick Text mining can also be used to rate call center. The Insurance Industry is among the ones that most can benefit from the application of technologies for the intelligent analysis of free text (known as Text Analytics, Text Mining or Natural Language Processing). 2. What were BBVA's challenges? How did BBVA over-come them with text mining and social media analysis? 3. In what other areas, in your opinion, can BBVA use. I would like to find different patterns recognition algorithm to detect different type of fraud. I have 1 million unstructured text documents about the clients. Value and benefits of text mining. Vast amounts of new information and data are generated everyday through economic, academic and social activities.
Text and data mining Text and data mining Find a better way to download, search, filter and understand millions of articles and books published on ScienceDirect. I would like to find different patterns recognition algorithm to detect different type of fraud. I have 1 million unstructured text documents about the clients. Feb 22, 2010 · Three Real-World Applications of Text Mining to Solve Specific Business Problems Analytics Derick Text mining can also be used to rate call center.
Text mining is an analytical field which derives high quality information from text. Text mining is widely used in the industry when data is unstructured. Derived information can be provided in the form of numbers (indices), categories or clusters, summary of text. In this blog, we will focus on applications of text mining, workflow and example.
Text Mining Applications
1. Analyze open ended survey comments- Analysis of open ended comments is most common in the current market. When a particular survey is conducted, there are options for the customers to provide feedback to the company using open ends rather than constraining their opinions into particular dimension of scaling. Sometimes, these open ends are more than 5000 words and hence, human mind can’t gather and extract information. The best possible solution is to use text mining algorithms.
2. Analyze customer insurance/warranty claims, feedback forms, etc.- In insurance domain, warranty claims information are usually open-ended. For example, when a motor claim is filed, insured specifies reason of accident in textual comments and you can imagine how difficult and erroneous it can be to process huge number of motor claim by a company in a month.
3. Analyze sentiment of users against a particular product/campaign/reviews using social media data- Every company are worried about their brand, customer satisfaction and customer preference. It takes just seconds for a customer to go on internet and spread bad words about a company. Social media analytics uses text mining to compute sentiment of customer. It’s easy to identify core topic discussed among customers every day on social media using text mining.
4. Automatic processing of emails/images/messages etc.- Text mining algorithms are used for automatic classification of texts. In outlook, a user categorizes the emails into various folders/spam. Similarly, on a larger scale using text mining algorithms key topics can be identified and the emails can be automatically forwarded to desired department
5. Identify competitors performance- In business intelligence sector, identifying competitors performance, capabilities, products offered, identifying their target business line can be automatically processed using combination of web crawling and text mining.
6. Automatic document search- In recent months, researchers have focused on text mining to identify reference documents for their research. For example-You are a researcher and would want to figure out summary of a chapter in a document. There are two ways to go through; one is read the entire chapter or use text mining algorithms.
Workflow of Text Mining
1. Collect Data- Unstructured information from websites, emails, blogs, social media websites, user comments, etc.
2. Text Parsing- This step involves extraction of words, parts of speech tagging, word filtering (removing preposition, numbers, and punctuations), synonyms, tokenization, and stemming.
3. Text Filtering- Removing irrelevant terms, building stop word dictionary and removing stop words
4. Transformation- Building term frequency document matrix (TDM) or document term matrix (DTM), computing frequency term counts, and calculating SVD’s
5. Text Mining Algorithms- Hierarchal Clustering, Topic Extraction, LDA and Gibbs Algorithm, Text summarization using text blob noun phrase extraction, sentiment analysis by identifying polarity using naïve Bayesian theorem, and Boolean rules
6. Analysis, Insights & Recommendations- Relationship between key categories, fish bowl analysis, risk analysis, identifying gaps and recommending it to business and key stakeholders.
Text Mining Terminologies
1. Text cleanup- Removes hyperlinks, special characters, ads from web pages, remove figures and formulas from web pages and documents
2. Tokenization- Tokenization is the process to divide unstructured data into tokens such as words, phrase, keywords, and other elements.
3. Stemming-It’s a process used to bring words to their base form. E.g. “amazing”, “amazed”, and “amaze” can be described as “amaze” using stemming.
4. Parts of Speech Tagging- POS tagging involves tagging every word in the document and assigns part of speech-noun, verb, adjective, pronoun, single noun, plural noun, etc.
5. N-grams is a part of tokenization. Creation of n-grams are important to understand the data. E.g. “good” is a positive sentiment whereas “not” is neural but when you combine “not good” it’s a negative sentiment.
If you want to analyze “the quick red fox jumps over the lazy dog”
a. Bi-gram:- Combination of 2 words. E.g. “quick fox”- this determines that fox is quick whereas “lazy dog” determines that dog is lazy. Hence, this could be used as an analysis between fox and dog where former is determined by its quickness and latter by its laziness.
b. Tri-gram:- Combination of 3 words E.g. “red fox jumps” determines fox is red and fox can jump whereas “lazy brown dog” determines dog is brown and lazy.
If you would have to analyze it without using n-gram it will lead into inaccurate information. Data like “red” “fox” “jumps” “lazy” “brown” “dog” analyzed separately doesn’t makes sense.
We shall discuss Mathematical applications of text mining algorithms in the upcoming blogs.
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