What a Nasty day: Exploring Mood-Weather Relationship from ...

Motivation Challenges Model Experiments Conclusion

What a Nasty day: Exploring Mood-WeatherRelationship from Twitter

Jiwei Li1, Xun Wang2 and Eduard Hovy2

1Department of Computer Science, Stanford University2Language Technology Institute, Carnegie Mellon University

Oct 26, 2014

Jiwei Li1, Xun Wang2 and Eduard Hovy2 What a Nasty day: Exploring Mood-Weather Relationship from Twitter

Motivation

Sentiment Analysis/ Extraction:

Sentence

Document

Feature Based Algorithms

SVM (Joachims., 1999)

CRF (Lafferty e al., 2001)

LDA, sLDA (Blei et al., 2003)

Neural Network (Socher et al., 2013)

Motivation

Sentence

Document

Motivation

Sentence

Document

Motivation

Sentence

Document

Motivation

Sentence

Document

Motivation

Sentence

Document

Motivation

Long-term Sentiment Analysis

Sentiment towards an entity over a long period of time.

resort

political issues/ figures

News Articles, Diplomatic Relations

Motivation

resort

Motivation

resort

Motivation

resort

Motivation

resort

News Articles,

Diplomatic Relations

Motivation

resort

Motivation

resort

Motivation

resort

Motivation

The People’s Daily : Governmental Newspaper in People’sRepublic of China

1960s: US Imperialism and its lackeys.

2000s: A healthy, steady and developmental relationshipbetween China and US, conforms to the fundamental interestsin both countries.

More technical, less political ! !

Motivation

Long-term Sentiment AnalysisThe People’s Daily : Governmental Newspaper in People’sRepublic of China

Motivation

Outline

Challenges

Experiments

Conclusion

Challenges

Challenge: Multiple entities, multiple sentiments.

Challenges

Johnson’s GovernmentNot a coreference problem

Challenges

Johnson’s Government

Not a coreference problem

Challenges

Heavy use of linguistic phenomenon:

rhetoric

metaphor (tiger made of paper)

proverb

nicknames

Challenges

rhetoric

proverb

nicknames

Challenges

rhetoric

proverb

nicknames

Challenges

rhetoric

proverb

nicknames

Challenges

rhetoric

proverb

nicknames

Assumptions

In a single news article, sentiment towards an entity isconsistent.Negative (USA) → Positive entities irrelevant

Over a certain period of time, sentiments towards an entityare inter-related.Negative (USA) → (Negative) tiger made of paper

Assumptions

In a single news article, sentiment towards an entity isconsistent.

Negative (USA) → Positive entities irrelevant

Assumptions

Over a certain period of time, sentiments towards an entityare inter-related.

Negative (USA) → (Negative) tiger made of paper

Assumptions

Challenges

Experiments

Conclusion

Notations:Sentiment score ranging m (1-7)

Antagonism

Tension

Disharmony

Neutrality

Goodness

Friendliness

Brotherhood

Notations:

For specific entity e (e.g., USA, Vietnam)

Sentence s: ms (sentiment toward e at current sentence)

Document d: md (sentiment toward e at current document)

Time period t (3 months): mt (sentiment toward e at currenttime).

Notations:

Markov Property at time level:

mt ∼ Poisson(mt−1)

Sample document sentiment from time sentiment

md ∼ Poisson(mt)

Sample sentence sentiment from document sentiment

mS ∼ Poisson(md)

Bootstrapping

Gibbs sampling for sentiment score at time- document- levelgiven sentence-level score.

Given document-level score, expand entity cluster, subjectivelexicon.

Update sentience level score

Bootstrapping

Input:

Entity: Vietnam

Sentiment score at document level md

Small collection of subjective lexicon: {great, 7}, {evil , 1}T = {Vietnam} : List of entities.

Input:

Entity: Vietnam

Input:

Entity: Vietnam

Input:

Entity: Vietnam

Small collection of subjective lexicon: {great, 7}, {evil , 1}

T = {Vietnam} : List of entities.

Input:

Entity: Vietnam

A is great

B is heroicC is evil.

Albania Workers’ party is the glorious party of Marxism andLeninismWe strongly warn Soviet Revisionism.

A is greatB is heroic

C is evil.

A is greatB is heroicC is evil.

Albania Workers’ party is the glorious party of Marxism andLeninism

We strongly warn Soviet Revisionism.

Target/Expression Extraction

Segment based Sequence Model:Semi-CRF (Sarawagi and Cohen, 2004; Yang and Cardie, 2013)

600 manually labeled sentences

Features

word, part of speech tag, word length, NER

window context (token, NER, POS ...)

Subjectivity lexicon features

Syntactic features, e.g., VPcluster, VPpred, VParg (Yang andCardie, 2013)

Features

Summary of Model:

Identify Target/Expression for sentences.

Obtain score for a few sentences whose expressions are foundin the subjective list.

Gibbs sampling to estimate document- time- level sentimentscore.

Bootstrapping: expand subjective list and entity cluster.

Summary of Model:

Some details

Strict error prevention in bootstrapping.

Ignore sentences with multiple targets or with negations.Ignore long or short sentences.

ICTCLAS for Chinese segmentation (Zhang et al., 2003)

Chunk consecutive words.Example: [ People’s / Army ].Chinese encyclopedia (e.g., Baidu encyclopedia and ChineseWikipedia)

Compound sentences are segmented into clauses based onparse trees.

Some details

Challenges

Experiments

Conclusion

Experiments

Target/Expression ExtractionSingle: Sentences with only one target.

Comparing Semi-CRF with CRF

Experiments

Target/Expression ExtractionSingle: Sentences with only one target.

Comparing Semi-CRF with CRF

Experiments

Target/Expression ExtractionSingle: Sentences with only one target.Comparing Semi-CRF with CRF

Experiments

Subjective Lexicon

Experiments

Foreign Relation Evaluation

Gold Standards: Qualitative Political Analysis

Institute of Modern International Relations, Tsinghua University,

Experiments

Evaluation Matrics: Pearson Correlation

Baselines

Bootstrapping+NoTime

Document-level SVR (Joachims, 1999; Pang and Lee, 2008),bag of words

Supervised-LDA

Experiments

Baselines

Supervised-LDA

Experiments

Baselines

Supervised-LDA

Experiments

Analysis

We have no permanent allies, no permanent friends, but onlypermanent interests -Lord Palmerston

Experiments

Analysis

Experiments

Analysis

Experiments

The enemy of my enemy is my friend -Arabic proverb

Experiments

The enemy of my enemy is my friend -Arabic proverb

Experiments

Conclusion

We propose a semi-supervised algorithm that harnesses higherlevel information (i.e., document-level, time-level).

Quantitative evaluation of diplomatic relations that mayfacilitate political scientists’ work.

Conclusion

Target/ Expression dataset:http://web.stanford.edu/~jiweil/dataset

Thank you !

Questions, Suggestions

Thank you !

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