Text Mining : Experience
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This document discusses text mining and its applications. It begins by defining text mining as sifting through vast collections of unstructured data beyond what traditional data mining tools can handle. It then covers various text mining tasks like information extraction, topic detection, summarization, and opinion mining. Applications discussed include tech mining, expert finders, question answering systems, and sentiment analysis of online reviews and social media. The document concludes by noting the increasing importance and opportunities of text mining as more data is generated.
Text Mining : Experience
- 1. ประสบการณ์การวิเคราะห์ข้อมูลด้วย วิธีการทาเหมืองข้อมูล (Text Mining) ดร.อลิสา คงทน นักวิจัย ห้องปฏิบัติการวิจัยวิทยาการมนุษยภาษา ศูนย์เทคโนโลยีอิเล็กทรอนิกส์และคอมพิวเตอร์แห่งชาติ 1
- 2. Text Mining is about… “Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.” Tapping the Power of Text Mining Communications of the ACM, Sept. 2006 2
- 3. Humans VS. Computers • Humans: Ability to distinguish and apply linguistic patterns to text – Could overcome language difficulties such as slangs, spelling variations, contextual meaning • Computers: Ability to process text in large volumes at high speed – Could sift through a large collection of texts to find simple statistics and relationship among terms in an instant of time • Text mining requires a combination of both Human's linguistic capability + computer's speed and accuracy NLP Data Mining
- 4. Text Mining Tasks • Information extraction: – Analyze unstructured text and identify key words or phrases and relationships within text • Topic detection and tracking: – Filter and present only documents relevant to the user profile • Summarization: – Text summarization reduces the content by retaining only its main points and overall meaning 4
- 5. Text Mining Tasks • Categorization: – Automatic classify documents into predefined categories • Clustering: – Group similar documents based on their similarity • Concept Linkage – Connect related documents by identifying their shared concepts, helping users find information they perhaps wouldn't have found through traditional search methods 5
- 6. Text Mining Tasks • Information Visualization – Represent documents or information in graphical formats for easily browsing, viewing, or searching • Question and answering (Q&A) – Search and extract the best answer to a given question 6
- 7. Applications: Tech Mining • Tech Mining is the application of text mining tools to science and technology (S&T) information particularly bibliographic abstracts • It exploits the S&T databases to see patterns, detect associations, and foresee opportunities 7
- 8. Tech Mining Process 8
- 9. Technical Intelligences: Who, What, When, Where? • Digest multiple S&T information resources • Profile Research Domains: – Who? – What? – When? – Where? • Map Relationships: Topics & Teams • Analyze Trends: What’s Hot & What’s Coming • And do so -- Quickly 9
- 10. What if I don’t have Tech Mining Software? 10
- 11. What if I don’t have Tech Mining Software? 11
- 12. Output example from Tech Mining Software Source: A.L. Porter, QTIP: quick technology intelligence processes, Technol. Forecast. Soc. Change 72 (2005) 12
- 13. Applications: Expert Finder 13
- 14. Applications: Expert Finder 14
- 15. Applications: Expert Finder 15
- 16. Applications: ABDUL (Artificial BudDy U Love) • An online information service which currently provides access to Thai linguistic (e.g., dictionary and sentence translation) and information resources (e.g., weather condition, stock price, gas price, traffic condition, etc.) • Users are able to use natural language to interact with ABDUL via Instant Messaging (IM) based protocol, Web browser, and Mobile devices 16
- 17. Applications: ABDUL (Artificial BudDy U Love) 17
- 18. Applications: ABDUL (Artificial BudDy U Love) 18
- 19. Web 1.0 VS. Web 2.0 19
- 20. User-Generated Contents • With the Web 2.0 or social networking websites, the amount of user-generated contents has increased exponentially • User-generated contents often contain opinions and/or sentiments • An in-depth analysis of these opinionated texts could reveal potentially useful information, e.g., – Preferences of people towards many different topics including news events, social issues and commercial products 20
- 22. Characteristics of Online Reviews • Natural language and unstructured text format • Some reviews are long and contain only a few sentences expressing opinions on the product • Could be difficult for a potential reader to understand and analyze each review that maybe relevant to his or her decision making 22
- 23. Opinion Mining • Opinion mining and sentiment analysis is a task for analyzing and summarizing what people think about a certain topic • Opinion mining has gained a lot of interest in text mining and NLP communities • Three granularities of opinion mining: – Document level – Sentence level – Feature level 23
- 24. Feature-Based Opinion Mining • This approach typically consists of two following steps: 1. Identifying and extracting features of an object, topic or event from each sentence 2. Determining whether the opinions regarding the features are positive or negative 24
- 25. Opinion Mining on Hotel Reviews in Thailand (Graphical Display) 25
- 26. Opinion Mining on Hotel Reviews in Thailand (Textual Display) 26
- 27. Comparison among Hotels 27
- 28. Opinion Mining on Mobile Network Operators in Thailand 28
- 29. Opinion Mining on Mobile Network Operators in Thailand 29
- 30. Challenges in Text Mining • Text Mining = NLP + Data Mining • Statistical NLP – Ambiguity – Context – Tokenization Sentence Detection – POS tagging • Data Mining – Ability to process the data – Massive amounts of data – Determining and extracting information of interest 30
- 31. Conclusions • As the amount of data increases, text-mining tools that sift through it will be increasingly valuable • Various applications for academic and industry uses 31
- 32. Thank you for your attention Q&A 32