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Text Mining Patrick Cash. Outline Introduction Data Mining Text Mining –Text Mining Process Text Mining Applications Challenges in Text Mining Conclusion.

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Presentasi berjudul: "Text Mining Patrick Cash. Outline Introduction Data Mining Text Mining –Text Mining Process Text Mining Applications Challenges in Text Mining Conclusion."— Transcript presentasi:

1 Text Mining Patrick Cash

2 Outline Introduction Data Mining Text Mining –Text Mining Process Text Mining Applications Challenges in Text Mining Conclusion

3 Introduction Why Text Mining? –Massive amount of new information being created World’s data doubles every 18 months (Jacques Vallee Ph.D) –80-90% of all data is held in various unstructured formats –Useful information can be derived from this unstructured data

4 Introduction Intelligence in text mining is based on NLP techniques Can be used as a preprocessing technique to harvest data and get an initial understanding of the patterns that exist in the data Often seen as a special case of data mining but there is an important difference

5 Data Mining Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information (or patterns) from data Data Mining: a misnomer? –Knowledge discovery, knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

6 Data Mining Descriptive: understanding underlying processes or behavior –Patterns and trends –Clustering Predictive: predict an unseen or unmeasured value –Future projections and missing values –Classification

7 SEMMA Search –Input data source, data sampling, partitioning Explore –Patterns, trends, outliers, visualization Modify –Clustering, feature reduction Model –Regression, tree, network Assess –Report, pass to next step in analysis

8 Search vs. Discover Data Mining Text Mining Data Retrieval Information Retrieval Search (goal-oriented) Discover (opportunistic) Structured Data Unstructured Data (Text)

9 Text Mining Many different by similar definitions Text Mining = Statistical NLP + Data Mining Text Mining is a process that employs –Statistical NLP: a set of algorithms for converting unstructured text into structured data objects –Data Mining: the quantitative methods that analyze these data objects to discover knowledge

10 Text Mining Descriptive –Pattern and trend analysis –Knowledge base creation –Summarization –Visualization Predictive –Classification –Question answering –Pattern and trend forecasting

11 Text Mining Techniques Information Retrieval –Indexing and retrieval of textual documents Information Extraction –Extraction of partial knowledge in the text Web Mining –Indexing and retrieval of textual documents and extraction of partial knowledge using the web (ontology building) Clustering –Generating collections of similar text documents

12 Text Mining Process

13 Text Preprocessing –Syntactic/Semantic text analysis Features Generation –Bag of words Features Selection –Simple counting –Statistics Text/Data Mining –Classification (Supervised) / Clustering (Unsupervised) Analyzing results

14 Text Mining Process Text preprocessing –Part Of Speech (POS) tagging Find the corresponding POS for each word. –Word sense disambiguation Context based or proximity based –Parsing Generates a parse tree (graph) for each sentence Each sentence is a stand alone graph

15 Text Mining Process Feature Generation –Text document is represented by the words it contains (and their occurrences) Order of words is not that important for certain applications (Bag of words) –Stemming: identifies a word by its root Reduce dimensionality –Stop words: The common words unlikely to help text mining

16 Text Mining Process Feature Selection –Reduce dimensionality Learners have difficulty addressing tasks with high dimensionality Only interested in the information relevant to what is being analyzed –Irrelevant features Not all features help

17 Text Mining Process Text Mining: Classification definition –Given: a collection of labeled records (training set) Each record contains a set of features (attributes), and the true class (label) –Find: a model for the class as a function of the values of the features –Goal: previously unseen records should be assigned a class as accurately as possible

18 Text Mining Process Text Mining: Clustering definition –Given: a set of documents and a similarity measure among documents –Find: clusters such that: Documents in one cluster are more similar to one another Documents in separate clusters are less similar to one another –Goal: Finding a correct set of documents clusters

19 Text Mining Process Supervised learning (classification) –The training data is labeled indicating the class –New data is classified based on the training set –Correct classification: The known label of test sample is identical with the class result from the classification model Unsupervised learning (clustering) –The class labels of training data are unknown –Establish the existence of classes or clusters in the data –Good clustering method: high intra-cluster similarity

20 Text Mining Process Analyzing the results –Are the results satisfactory? –Does more mining need to be done? –Does a different technique need to be used? –Does another iteration of one or more steps in the process need to be done?

21 Text Mining Applications Bioinformatics –Genomics research (DNA sequencing) Medical –Mining medical records to improve care Business intelligence –Risk analysis Research –Analyzing research publications Basically anywhere there is large amount of unstructured text data

22 Text Mining Application Classification (Categorization) –Spam detection, Document organization Clustering –Trend analysis, Topic identification Web Mining –Trend analysis, Opinion mining, Ontology creation Classical NLP –Text summarization, Question answering, Information extraction

23 Text Mining Application Smaller scale applications –Relationship Analysis If A is related to B, and B is related to C, there is potentially a relationship between A and C. –Trend analysis Occurrences of A peak in October. –Mixed applications Co-occurrence of A together with B peak in November. (Shopping Cart Analysis)

24 Challenges in Text Mining Remember –Text Mining = Statistical NLP + Data Mining Text mining suffers from the same challenges as Statistical NLP and Data Mining Add in the additional difficulties associated with the data not being structured

25 Challenges in Text Mining Statistical NLP –Ambiguity –Context –Tokenization \ Sentence Detection –Stemming –POS Tagging –Coreference Resolution

26 Challenges in Text Mining Data Mining –Data preprocessing Ability to process the data Massive amounts of data Determining and extracting information of interest –Availability of NLP tools to work with data mining –Discovery process No training data available

27 Conclusion Text Mining = Statistical NLP + Data Mining –Culmination of all the NLP techniques covered in this course Growing research area that will be important as information growth (and need to extract knowledge from that information) increases

28 References Even-Zohar, Y. Introduction to Text Mining. Supercomputing, 2002. Treloar, N AvaQuest Inc. ions/treloar/nathantreloar.ppt ions/treloar/nathantreloar.ppt Witte, R. Faculty of Informatics Institute for Program Structures and Data Organization (IPD) share/IntroductionToTextMining.pdf share/IntroductionToTextMining.pdf

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