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Extraction and Visualisation of Emotion from News Articles

Extraction and Visualisation of Emotion from News Articles. Eva Hanser, Paul Mc Kevitt. School of Computing & Intelligent Systems Faculty of Computing & Engineering University of Ulster, Magee hanser-e@email.ulster.ac.uk, p.mckevitt@ulster.ac.uk. News Visualisation – Emotion Extraction.

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Extraction and Visualisation of Emotion from News Articles

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  1. Extraction and Visualisation of Emotion from News Articles Eva Hanser, Paul Mc Kevitt School of Computing & Intelligent Systems Faculty of Computing & Engineering University of Ulster, Magee hanser-e@email.ulster.ac.uk, p.mckevitt@ulster.ac.uk

  2. News Visualisation – Emotion Extraction 1 Introduction – What is NewsViz? 2 Background – Related Projects 3 Design & Implementation – The NewsViz Application 4 Prototype Demonstration 6 Testing 7 Relation to Other Work 8 Conclusion & Future Work

  3. News Visualisation – Emotion Extraction What is NewsViz? From natural language to visual presentation: NewsViz automatically produces animations from text Input: Online News Article NewsViz System Output: Animation

  4. News Visualisation – Emotion Extraction What is NewsViz? Aim: • Animation embedded into news website Objectives: • Entertainment • Quick overview • Emotional aspects >> view website

  5. News Visualisation – Emotion Extraction What is NewsViz? • The Challenges: • 1. Natural Language Processing (computational interpretation of meaning of text) 2. Automatic creation of animations A manageable project: • Prototype limited to one topic: football news • Focus on determining emotional aspects • Reduced to background visualisation

  6. News Visualisation – Emotion Extraction Related Projects Qtag Syntactic Analysis (based on grammar): Part-of-Speech Tagging (e.g. Qtag) • identifying word types such as nouns, adjectives, verbs, … • 95-97% correct Tag-list Tagged text Bayern_VBMunich_NP stretched_VBD their_DPS lead_NN at_PRP the_AT top_NN as_CJS Hamburg_NP suffered_VBD a_AT tragic _JJ surprise_NN home_NN loss_NN._. PRP preposition JJ adjective, general NN noun, common singular NNS noun, common plural NP noun, proper singular VB verb, base from VBD verb, past tense . . . http://www.english.bham.ac.uk/staff/omason/software/qtag.html

  7. News Visualisation – Emotion Extraction Related Projects WordsEye WordsEye: Creates static 3D scenes from text input http://www.wordseye.com

  8. News Visualisation – Emotion Extraction Related Projects WordsEye WordsEye – Description and Rendered Image The skiff is on the ocean. The grassy mountain is 20 feet behind the boat. The dog is in the boat. The fishing pole is two feet in front of the dog. The bottom of the palm tree is below the bottom of the mountain. It is 20 feet behind the boat. http://www.wordseye.com

  9. Who? Does? What? News Visualisation – Emotion Extraction Related Projects WordsEye More Syntax Analysis: Structure of Sentences Dependency Parser (e.g. used in WordsEye) • Finding relations between words and phrases • Dependency rules http://www.wordseye.com

  10. News Visualisation – Emotion Extraction Related Projects WordsEye Graphical Database in WordsEye 3D objects, their attributes (colour, size, surface) http://www.wordseye.com

  11. Semantic Relation Synonymy (similar) Antonymy (opposite) Hyponymy (subordinate) Meronymy (part) Troponomy (manner) Entailment Examples pipe, tube sad, unhappy wet, dry rapidly, slowly maple, tree tree, plant wheel, car whisper, speak divorce, marry Syntactic Category N, V, Aj, Av Aj, Av, (N, V) N N V V News Visualisation – Emotion Extraction Related Projects WordNet Semantic Analysis (based on meaning): Lexical Knowledgebase (e.g. WordNet) sets of synonymous words and basic semantic relations http://wordnet.princeton.edu/

  12. News Visualisation – Emotion Extraction Related Projects Story Picturing Engine The Story Picturing Engine: matching keywords + image regions • step 1: filtering out common words (a, the, of, …) • step 2: identification of proper words (places and people involved) • step 3: similarity count of remaining keywords (words with too many meanings are too vague) • … further steps for image processing

  13. News Visualisation – Emotion Extraction Related Projects Story Picturing Engine Example text on walk through ParisH = highest ranked images, L = Lowest ranked images

  14. News Visualisation – Emotion Extraction NewsViz NewsViz Architecture

  15. News Visualisation – Emotion Extraction NewsViz Emotion Visualiser

  16. News Visualisation – Emotion Extraction NewsViz Graphics Database • Abstract Visuals for 4 Emotions 2 - boring 1 - sad 4 - happy 3 - tense

  17. News Visualisation – Emotion Extraction NewsViz Word Lexicon with Emotion Indices <word> <name>challenges</name> <mood>3</mood> <!– tension  <intensity>3</intensity> <synonyms></ synonyms > … </word> <word> <name>home</name> <mood>4</mood> <!– happy  <intensity>1</intensity> </word> <word> <name>gaps</name> <mood>1</mood> <!– sad  <intensity>2</intensity> </word>

  18. News Visualisation – Emotion Extraction NewsViz Summarization Options

  19. News Visualisation – Emotion Extraction Demonstration

  20. News Visualisation – Emotion Extraction Demonstration

  21. News Visualisation – Emotion Extraction Demonstration

  22. News Visualisation – Emotion Extraction Demonstration

  23. News Visualisation – Emotion Extraction Demonstration

  24. News Visualisation – Emotion Extraction Demonstration

  25. News Visualisation – Emotion Extraction Demonstration

  26. News Visualisation – Emotion Extraction Demonstration

  27. News Visualisation – Emotion Extraction Demonstration

  28. News Visualisation – Emotion Extraction Testing • Procedure • • NewViz performance evaluated against human interpretation: • 1. General mood course (3-5 emotions per text) • 2. 1-2 Emotions per sentence • • types of emotion extraction error • Falsely detected emotion : 0 points • Missing emotion : points depending on significance • Overall feeling represented, 2-3 points • Similar emotion : 4 points • Exact emotion: 5 points

  29. News Visualisation – Emotion Extraction Testing Results• Course of moods mostly identified correctly • Word-by-Word method highest correctness but too fine grained for animation • Best results with both (adjective and nouns)

  30. News Visualisation – Emotion Extraction Conclusion & Future Work Summary • Emotional interpretation of online news articles • Course of moods depicted in abstract 2D animations • Different methods of language processing • Satisfactory outcome User Evaluation • Appreciation of animations as quick overviews Future Work • Extension of knowledge bases • Inclusion of different topics • Improvement of summarisation, e.g dependency parser

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