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Opinion Mining of Customer Feedback Data on the Web

Opinion Mining of Customer Feedback Data on the Web. Dongjoo Lee School of Computer Science and Engineering, Seoul National University Seoul 151-742, Republic of Korea therocks@europa.snu.ac.kr Ok-Ran Jeong Department of Computer Science, University of Illinois at Urbana- Champaign

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Opinion Mining of Customer Feedback Data on the Web

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  1. Opinion Mining of Customer Feedback Data on the Web Dongjoo Lee School of Computer Science and Engineering, Seoul National University Seoul 151-742, Republic of Korea therocks@europa.snu.ac.kr Ok-Ran Jeong Department of Computer Science, University of Illinois at Urbana- Champaign Urbana, IL, 61801, USA orjeong@uiuc.edu Sang-goo Lee School of Computer Science and Engineering, Seoul National University Seoul 151-742, Republic of Korea sglee@europa.snu.ac.kr Presented By Dongjoo Lee, Intelligent Databases Systems Lab.

  2. Customer Feedback Data Marketing planner Potential customer This camera is my first digital one and was super easy to learn to use. The picture looks great and it’s simple to get the correct exposure. The memory card that comes with the camera has a very small capacity though, (it holds about 4 photos) so a separate memory card is a necessity. I’m not very happy with the memory card.” This camera is my first digital one and was super easy to learn to use. The picture looks great and it’s simple to get the correct exposure. The memory card that comes with the camera has a very small capacity though, (it holds about 4 photos) so a separate memory card is a necessity. I’m not very happy with the memory card.”

  3. Goal of OM Ratio of pos and neg opinions pos neg 123 20 Serves the chosen objectives Opinion summary Effective presentation lens …… Automatic or semi-automatic exposure … memory ……… … From reliable amounts of feedback data …

  4. Tasks for Opinion Mining

  5. Development of Linguistic Resource (1) negative positive bad good terrible excellent subjective more intensive vertical yellow liquid objective • Linguistic resources • Used to extract opinion and to classify the sentiment of text • Appraisal theory • A framework of linguistic resources which describes how writers and speakers express inter-subjective and ideological position • Sentiment related properties • Subjectivity, Orientation, Strength

  6. Development of Linguistic Resource (2) negative positive seed terms corpus and but • Conjunction method • Hatzivassiloglou and McKeown (1997) • adjectives in ‘and’ conjunctions usually have similar orientation, while ‘but’ is used with opposite orientation. • PMI (Pointwise Mutual Information) method • Turney and Littman (2003), Baroni and Vegnaduzzo (2004) • terms with similar orientation tend to co-occur in documents • subjective adjectives tend to occur in the near of other subjective adjectives • WordNet Exploring method • Hu et al. (2004) • adjectives usually share the same orientation as their synonyms and opposite orientation as their antonyms • Gloss Classification method • Esuli et al. (2005, 2006) • terms with similar orientation have similar glosses • terms without orientation have non-oriented glosses • SentiWordNet

  7. Sentiment Classification • The process of identifying the sentiment – or polarity – of a piece of text or a document. • PMI method • Turney et al. (2002) • SO(phrase) = PMI(phrase, “excellent”) – PMI(phrase, “poor”) • Machine Learning method • A special case of text categorization with sentiment- rather than topic-based categories • Pang and Lee (2002) • Default Classifier - Naïve Bayes, MaxEnt, SVM, PrTFIDF • Pang and Lee (2004) • Use only subjective parts • NLP Combined method • Whitelaw et al. (2005) • Applied the appraisal theory • Wilson et al. (2005) • Employ machine learning and 28 linguistic features

  8. Systems for Opinion Summarization higher precision and lower recall

  9. Discussion Provide an overall picture of the tasks and techniques for opinion mining system. Focused on surveying and analyzing the methods for development of linguistic resources, sentiment classification, and opinion summarization. Opinion mining has become important for all types of organizations, including for-profit corporations, government agencies, educational institutions, non-profit organizations, and the military in gauging the opinions, likes and dislikes, and the intensity of the likes and dislikes, of the products, services, and policies they offer and plan to offer. An understanding of the overall picture of the tasks and techniques involved in opinion mining is of significant importance.

  10.  Thank You. therocks@europa.snu.ac.kr

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