Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Topic & Sentiment Detection on Twitter and Facebook

The goal of this project is to produce a programming framework for the social interaction side of knowledge processing. We aim at making museum content available outside of the Amsterdam Museum by detecting conversational topics on social media and their corresponding emotional/ engagement level. We use API’s for Facebook, Twitter and Google Places to access information about the users specific location and preferences linked to the four values of DNA. Via several classification methods, we distinguish between relevant and irrelevant information. Relevant information consists of posts, hashtags and retweets of the user containing a multitude of keywords related to DNA. The irrelevant information embodies marketing post made by corporations and spam and are not taken into account. As collecting and classifying the via social media gathered data occurs at an early stage, our group is located in the front of the chain collaboration. Our main goal is to gather and classify the filtered relevant information and pass this on to the second in chain for further processing, this would be the story engine group who can use this to create situation-specific stories in real-time and possibly the presentation groups.

  • Als Erste(r) kommentieren

Topic & Sentiment Detection on Twitter and Facebook

  1. 1. Topic & Sentiment Detection on Twitter and Facebook Sylvia van Schie Wouter Stuifmeel Vincent Velthuis Knowledge Based Media Systems | March 2013
  2. 2. Index 1. Project 2. Processing 3. Method 4. Sentiment Analysis 5. Topic Detection 6. Keywords 7. Example 8. Collaboration 9. Difficulties 2/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  3. 3. Project DNA Themes – Freedom of Thought – Creativity – Spirit of Enterprise – Civic Virtue • Access information by using API's (Facebook, Twitter, Google Places) • Classify posts/tweets to themes • Classify user’s sentiment 3/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  4. 4. Processing: General opinion of core themes within Amsterdam 5/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  5. 5. Method • [Twitter-only] Insert hashtags to “term table” and replace hashtags with regular words • Delete URL’s and Links from text • Natural Language Processing • Sentiment Detection • Delete irrelevant terms using NLP • Insert remaining terms to “term table” • Contents of “term table” to Wordnet • Classify to Themes 4/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  6. 6. Sentiment Analysis OpinionFinder (Wiebe & Mihalcea) Sentiment Polarity Adjectives, Adverbs and Verbs (NLP) Positive [+], Negative [-] or Neutral [0] Weak (+/- 1) or strong (+/- 5) Sentiment For example • Great is strong positive (+5), dull is weak negative (-1) 6/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  7. 7. Topic Detection Topics are the 4 DNA themes • Semantic deduction via Wordnet • Compare Wordnet meaning with list of keywords related to DNA themes • Classify to themes according to overlap 7/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  8. 8. Core Themes of DNA (#keywords) Appendix A: Core Themes of DNA Civic Virtue (Burgerschap) Church / Cathedral / Basilica / Chapel / Mosque / Synagogue Catholic / Protestant / Jewish / Muslim / Semitic / Religion / God Politic / Politicians / Government / Civic government / Society Social support / Social Security / Payment / Community / Clan / Alliance / Union / Nation Freedom of Thought (Vrijdenken) Church / Cathedral / Basilica / Chapel / Mosque / Synagogue Catholic / Protestant / Jewish / Muslim / Semitic / Religion / God Religious / Tolerance / Free Thinking / Open-mindedness / Understanding / Sympathy / Freedom / Humanity / Mercy / Kindness / Sympathy / Charity / Compassion / Drugs / Weed / Doges / Coffeeshop / Marihauna / Dope / Legalisation / Prostitution / Red Light Distrcit / Hookers / Whores / Prostitutes / Legalisation / Squatters / Squat / Homesteader / Settler / Homosexuality / Gay Marriage / Homophile / Free Speech / Uncensored / Censorship / Opinion / Liberty / Education / Teaching / School / University / Instruction 8/14 Creativity (Creativiteit) Artists / Painters / Sculptors / Designers / Composer / Inventor / Creator / Painting / Building / Statue / Inventions / Inventive / Music / Sonet / Composition / Concert / Symphony / Musician / Song Museum / Exhibition / Library / Gallery / Concert hall Academics / University / Researchers / Science Creativity / Culture / Original / Vision / Inspiration / Innovation / Creative Minds / Rembrandt / van Gogh / Vermeer / Brood / Multicultural / Cultural Spirit of Enterprise (Ondernemerschap) Settlement / City / Town / Harbour Commerce / Trade / Economics / World Trade Agriculture / Farming / Fishery / Cultivation Transportation / Import / Export / Shipping / Shipment Spirit of enterprise / Commercial enterprise / Golden Age / Ship / VOC / EAC / Finance / Money / Profit / Capital / Resources / Wealth / Slavery / Exploitation / Enslavement / Servitude / Innovation / Industry / Business / Organization / Entrepreneurship Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  9. 9. Wouter @myshuno 1day The exhibit about slavery was really great! #Amsterdam #YOLO Expand | Reply | Retweet | Favorite | More • “The exhibit about slavery was really great.” • S( NP(( DT NN ) Adj ( NN )) VP( VN( Adv Adj ))) • SA looks at Adj, Adv and VN – about = neutral (0) – was = neutral (0) – really = ‘booster’ for great – great = strong positive (+5) – Overall sentiment is positive (+) Example (1) 9/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  10. 10. Example (2) • Insert NN’s into “Term table” – ‘exhibit’ and ‘slavery’ • Wordnet semantics – S: (n) display, exhibit, showing (something shown to the public) "the museum had many exhibits of oriental art“ – S: (n) slavery, slaveholding (the practice of owning slaves) • Keyword comparison gives us two themes: – Creativity and Spirit of Enterprise 10/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  11. 11. Collaboration (1) WP2: Story Engine – Input: DNA Themes – Input: User’s sentiment 11/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013 Sentiment DNA Positive Negative Neutral Creativity CR[+] CR[-] CR[0] Spirit of Enterprise EN[+] EN[-] EN[0] Freedom of Thought FT[+] FT[-] FT[0] Civic Virtue CV[+] CV[-] CV[0]
  12. 12. Collaboration (2) WP4: Presentation – Input: Timestamp GPS • Easy to extract via Facebook and Twitter API’s • Coordinates by Google Places 12/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  13. 13. Collaboration (2) 13/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013
  14. 14. Difficulties • Grammar is not always correct on Facebook and Twitter – Ignore posts who don’t give results • Sentiment is not always easy to classify (for example by sarcasm) – Default sentiment is neutral [0] – Work in progress in the field 14/14Knowledge Based Media Systems | Topic & Sentiment Detection on Twitter and Facebook | S. van Schie, W. Stuifmeel and V. Velthuis | March 2013

×