Extraction and Visualisation of Emotion from News Articles Eva Hanser, Paul Mc Kevitt School of...

Extraction and Visualisation of Emotion from News Articles

Eva Hanser, Paul Mc Kevitt

School of Computing & Intelligent SystemsFaculty of Computing & EngineeringUniversity of Ulster, [email protected], [email protected]

News Visualisation – Emotion Extraction

1 Introduction – What is NewsViz?

2 Background – Related Projects3 Design & Implementation – The NewsViz Application

4 Prototype Demonstration

6 Testing7 Relation to Other Work8 Conclusion & Future Work

What is NewsViz?

From natural language to visual presentation:NewsViz automatically produces animations from text

Input:


OnlineNewsArticle

Animation

NewsVizSystem

Output:

Aim: • Animation embedded into news website

Objectives: • Entertainment• Quick overview• Emotional aspects

>> view website

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

What is NewsViz?


Syntactic Analysis (based on grammar):

Part-of-Speech Tagging (e.g. Qtag) • identifying word types such as nouns, adjectives, verbs, …• 95-97% correct

Qtag

Tag-list Tagged text

Bayern_VB Munich_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, generalNN noun, common singular NNS noun, common pluralNP noun, proper singularVB verb, base fromVBD verb, past tense. . .

http://www.english.bham.ac.uk/staff/omason/software/qtag.html

Related Projects


WordsEye: Creates static 3D scenes from text input

http://www.wordseye.com

WordsEye Related Projects


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.



More Syntax Analysis: Structure of Sentences

Dependency Parser (e.g. used in WordsEye)• Finding relations between words and phrases• Dependency rules

Who? Does? What?







Graphical Database in WordsEye

3D objects, their attributes (colour, size, surface)

Semantic Analysis (based on meaning):Lexical Knowledgebase (e.g. WordNet) sets of synonymous words and basic semantic relations

Semantic Relation

Synonymy (similar)

Antonymy (opposite)

Hyponymy (subordinate)

Meronymy (part)Troponomy (manner)Entailment

Examples

pipe, tubesad, unhappywet, dryrapidly, slowlymaple, treetree, plantwheel, carwhisper, speakdivorce, marry

Syntactic Category

N, V, Aj, Av

Aj, Av, (N, V)

N

NVV

http://wordnet.princeton.edu/

WordNet Related Projects


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

Story Picturing Engine Related Projects


Example text on walk through ParisH = highest ranked images, L = Lowest ranked images

Story Picturing Engine Related Projects


NewsViz Architecture

NewsViz


Emotion Visualiser

NewsViz


Graphics Database • Abstract Visuals for 4 Emotions

2 - boring

4 - happy3 - tense

1 - sad

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>

NewsViz


Summarization Options

NewsViz


Demonstration


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 pointsMissing emotion : points depending on

significanceOverall feeling represented, 2-3 points

Similar emotion : 4 pointsExact emotion: 5 points

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)

Testing


Method Word by Sentence Threshold average

Word based 2 3

Word type correct grain correct grain correct grain correct grain

adjectives 3.125 12 3.25 7.5 2.375 5 1.25 2.3 2.5nouns 3.875 31 2.625 9.3 2.875 14 2 4.8 2.844both 4 33 2.75 9.5 3.5 18 1.5 10 2.938average 3.667 25 2.875 8.8 2.917 12 1.583 5.7

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

Conclusion & Future Work


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