Opinion Mining


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  • What is lexicon development about?
    Regarding the term “polarity”: There are other terms that people in the field use to talk about polarity: semantic orientation and valence are two common ones.
  • Step 4: the goal is to have mainly same-orientation links within the subsets and different-orientation links across the subsets
  • Opinion Mining

    1. 1. Opinion Mining
    2. 2. Outline       Definition Applications Challenges Model Conclusion References
    3. 3. Definition  Opinion mining (sentiment mining, opinion/sentiment extraction) is the area of research that attempts to make automatic systems to determine human opinion from text written in natural language.  It seeks to identify the view point (s) underlying a text span; an example application is classifying a movie review as thumbs up or thumbs down.
    4. 4. Opinion mining is a new discipline which has recently attracted increased attension within fields such as Marketing,Recomandation systems and financial market prediction.Although often associated Emotional states from text,Opinion Mining is an independent area related to Natural Language Processing And Text mining that deals with the Identification of opinionsAnd attitudes in . Natural Language Text
    5. 5.  Consider, for instance, the following scenario. A major computer manufacturer, disappointed with unexpectedly low sales, finds itself confronted with this question: Why aren't consumers buying our laptop?
    6. 6.   What other people think has always been an important piece of information for most of us during the decision-making process. Opinion mining draws on computational linguistic, information retrieval, text mining, natural language processing, machine learning, statistics and predictive analysis
    7. 7.  1. 2.   Two main types of textual information. Facts Opinions Most current information processing technique (e.g., search engines) work with facts (assume they are true) Facts can be expressed with topic keywords
    8. 8. In real life, facts are important, but opinion also plays a crucial role. A computer manufacturer, disappointed with low sales, asks itself: Why aren’t consumers buying our laptop? The Democratic National Committee, disappointed with the last election, wants to know on an on-going basis: What is the reaction in the press, newsgroups, chat rooms, and blogs to Bush’s latest policy decision? 
    9. 9.  The main advantage is the speed On average, humans process six articles per hour against the machine’s throughput of 10 per second
    10. 10. Applications    recommendation systems Summarization Applications in Business  marketing intelligence,  product and service benchmarking and improvement.  To understand the voice of the customer as expressed in everyday communications
    11. 11. Applications  Politics As is well known, opinions matter a great deal in politics. Some work has focused on understanding what voters are thinking
    12. 12. Challenges The difficulty lies in the richness of the language that human use. Example: 1. This is a great camera. 2. A great amount of money was spent for promoting this camera. 3. One might think this is a great camera. Well think again, because.....  a single keyword can be used to convey three different opinions, +ve, neutral and -ve respectively. 
    13. 13. Challenges  In order to arrive at sensible conclusions, sentiment analysis has to understand context. For example, “fighting” and “disease” is negative in a war context but positive in a medical one.  Different mining for different domains.
    14. 14. sentiment analysis model
    15. 15. Data Preparation  The data preparation step performs necessary data preprocessing and cleaning on the dataset for the subsequent analysis. Some commonly used preprocessing steps include removing non-textual contents and markup tags (for HTML pages), and removing information about the reviews that are not required for sentiment analysis, such as review dates and reviewers’ names.
    16. 16. Review Analysis  The review analysis step analyzes the linguistic features of reviews so that interesting information, including opinions and/or product features, can be identified.  This step often applies various computational linguistics tasks to reviews first, and then extracts opinions and product features from the processed reviews.
    17. 17. Sentiment Classification  There are two main techniques for sentiment classification:  The symbolic technique uses manually crafted rules and lexicons, The machine learning approach uses unsupervised, or supervised learning to construct a model from a large training corpus. 
    18. 18. ?What  Find relevant words, phrases, patterns that can be used to express subjectivity  Determine the polarity of subjective expressions
    19. 19. Words   Adjectives positive: honest important mature large patient Ron Paul is the only honest man in Washington.    Kitchell’s writing is unbelievably mature and is only likely to get better. To humour me my patient father agrees yet again to my choice of film negative: harmful hypocritical inefficient insecure   It was a macabre and hypocritical circus. Why are they being so inefficient ?
    20. 20. Words  Verbs positive: praise, love  negative: blame, criticize   Nouns positive: pleasure, enjoyment  negative: pain, criticism 
    21. 21. Phrases  Phrases containing adjectives and adverbs   positive: high intelligence, low cost negative: little variation, many troubles
    22. 22. Machine Learning  Studies showed that standard machine learning techniques definitively outperform humanproduced baselines.  To treat sentiment classification simply as a special case of topic-based categorization (with the two “topics” being positive sentiment and negative sentiment)
    23. 23. Supervised Methods  In order to train a classifier for sentiment recognition in text, classic supervised learning techniques (e.g. Support Vector Machines, naive Bayes, Maximum Entropy) can be used. A supervised approach entails the use of a labelled training corpus to learn a certain classification function. The method that in the literature often yields the highest accuracy regards a Support Vector Machine classifier
    24. 24. Suport Vector Machine
    25. 25. Unsupervised Learning A clustering algorithm partitions the adjectives into two subsets + slow scenic nice terrible handsome painful fun expensive comfortable
    26. 26. Conclusion     An important field of study New Field Many applications Almost no work in this area
    27. 27. References   Pang, Bo and Lee, L. (2008). “Opinion Mining and Sentiment Analysis”, Foundations and Trends R in, Information Retrieval, Vol. 2, Nos. 1–2 (2008) 1– 135, ebook from http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf Wiebe, J. Cardie, C. and Riloff, E. ( 2007). “Manual and Automatic Subjectivity and Sentiment Analysis” , Center for Extraction and Summarization of Events and Opinions in Text. University of Utah