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Deep Tweets: from Entity Linking
to Sentiment Analysis
Pierpaolo Basile, Valerio Basile, Malvina Nissim, Nicole Novielli
{...
Timeline of Tasks
SemEval‘13
Sentiment Analysis
in Twitter
SemEval‘14
- Sentiment
Analysis in Twitter
- Aspect Based
Senti...
SENTIPOLC @Evalita 2014
• Tasks
– Subjectivity Classification
– Polarity Classification (most popular)
– Irony Detection
•...
Timeline of Tasks
#Micropost2014
Named Entity Extraction
and Linking (NEEL)
#MSM2013
Concept Extraction
Challenge
SemEval‘...
Evalita 2016?
Entity-Based Sentiment Analysis
• Detecting the sentiment attached to an entity
in a tweet
• Stance detection
• Relevant f...
Annotation of Entities
@FabioClerici sono altri a dire che un reato.
E il "politometro" come lo chiama #Grillo vale
per tu...
Challenge-oriented Sentiment
Analysis?
• Prevalence of supervised ML systems in both
SemEval and Evalita
• Beyond the chal...
Distribution in SENTIPOLC Data
39%
61%
Positive Tweets
Negative Tweets
34%
66%
#Grillo #Monti
Sentiment Analysis of Figurative
Language
• Complex relation between sentiment and
figurative language
– Irony mainly acts...
Annotation of Irony
• Extension of the SENTIPOLC schema
subj pos neg irony opos oneg Description
1 1 0 1 0 1 Subjective tw...
Resources
• SENTIPOLC Dataset1
– Train set using tweets about political topic
• TWITA2
– Expand train set
– Test (no polit...
Conclusion and Open Issues
• Entity linking and sentiment analysis on Twitter are
challenging, attractive, and timely task...
Evalita 2016?
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Deep Tweets: from Entity Linking to Sentiment Analysis

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The huge amount of information streaming from online social networking is increasingly attracting the interest of researchers on sentiment analysis on micro-blogging platforms. We provide an overview on the open challenges of senti- ment analysis on Italian tweets. We discuss methodological issues as well as new directions for investigation with particular focus on sentiment analysis of tweets containing figurative language and entity- based sentiment analysis of micro-posts.

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Deep Tweets: from Entity Linking to Sentiment Analysis

  1. 1. Deep Tweets: from Entity Linking to Sentiment Analysis Pierpaolo Basile, Valerio Basile, Malvina Nissim, Nicole Novielli {pierpaolo.basile,nicole.novielli}@uniba.it {v.basile,m.nissim}@rug.nl
  2. 2. Timeline of Tasks SemEval‘13 Sentiment Analysis in Twitter SemEval‘14 - Sentiment Analysis in Twitter - Aspect Based Sentiment Analysis Evalita 2014 SENTIPOLC SemEval‘15 - Implicit Polarity of Events - Sentiment Analysis in Twitter - Sentiment Analysis of Figurative Language in Twitter - Aspect Based Sentiment Analysis SemEval‘16 - Sentiment Analysis in Twitter - Aspect Based Sentiment Analysis - Detecting Stance in Tweets
  3. 3. SENTIPOLC @Evalita 2014 • Tasks – Subjectivity Classification – Polarity Classification (most popular) – Irony Detection • Best system supervised (Uniba) – Two rule-based systems (Unibo, Ca’ Foscari-Venezia) – All ML systems supervised • Most popular task at Evalita 2014 – 11 Teams – 35 Submitted runs (only from research institutions) – Interest from industry
  4. 4. Timeline of Tasks #Micropost2014 Named Entity Extraction and Linking (NEEL) #MSM2013 Concept Extraction Challenge SemEval‘13 Sentiment Analysis in Twitter SemEval‘14 - Sentiment Analysis in Twitter - Aspect Based Sentiment Analysis Evalita 2014 SENTIPOLC SemEval‘15 - Implicit Polarity of Events - Sentiment Analysis in Twitter - Sentiment Analysis of Figurative Language in Twitter - Aspect Based Sentiment Analysis #Micropost2015 Named Entity Extraction and Linking (NEEL) SemEval’15 Multilingual All-Words Sense Disambiguation and Entity Linking SemEval‘16 - Sentiment Analysis in Twitter - Aspect Based Sentiment Analysis - Detecting Stance in Tweets
  5. 5. Evalita 2016?
  6. 6. Entity-Based Sentiment Analysis • Detecting the sentiment attached to an entity in a tweet • Stance detection • Relevant for modelling socio-economic phenomena – Mining political sentiment, predicting election results – Commercial application – Health issues
  7. 7. Annotation of Entities @FabioClerici sono altri a dire che un reato. E il "politometro" come lo chiama #Grillo vale per tutti. Anche per chi fa #antipolitica. FabioClerici (offsets 1-13) linked as NIL (no resources in DBpedia) Grillo (offsets 85-91) linked with the respective URI in DBpedia: http://dbpedia. org/resource/Beppe_Grillo
  8. 8. Challenge-oriented Sentiment Analysis? • Prevalence of supervised ML systems in both SemEval and Evalita • Beyond the challenge, are they valid in the real world? – Domain-dependence and low temporal validity – Political debates: countries afflicted by war – Technology: ‘killer’ features in positive reviews
  9. 9. Distribution in SENTIPOLC Data 39% 61% Positive Tweets Negative Tweets 34% 66% #Grillo #Monti
  10. 10. Sentiment Analysis of Figurative Language • Complex relation between sentiment and figurative language – Irony mainly acts as a polarity reverser – Metaphor, sarcasm and other linguistic devices might impact sentiment in different ways • Necessary treatment: > 20% of tweets show some form of figurative usage (irony/sarcasm)
  11. 11. Annotation of Irony • Extension of the SENTIPOLC schema subj pos neg irony opos oneg Description 1 1 0 1 0 1 Subjective tweet Positive literal polarity Negative overall polarity Botta di ottimismo a #lInfedele: Governo Monti, o la va o la spacca
  12. 12. Resources • SENTIPOLC Dataset1 – Train set using tweets about political topic • TWITA2 – Expand train set – Test (no political topic) • Italian dataset of manually annotated tweets for Named Entity Linking3 – Add sentiment annotation 1 - http://www.di.unito.it/~tutreeb/sentipolc-evalita14/data.html (Basile et al., 2014) 2 - http://valeriobasile.github.io/twita/about.html (Basile and Nissim 2013) 3 - https://github.com/swapUniba/neel-it-twitter (Basile et al., @CLIC 2015)
  13. 13. Conclusion and Open Issues • Entity linking and sentiment analysis on Twitter are challenging, attractive, and timely tasks for the Italian NLP community – Options: running the two tasks on shared data? – How does SA differ in message- and entity-level? Techniques, features, results. – How to deal with the layer of figurative language? – How is annotation affected? • How to prevent challenge-bound systems? – Train and test set from different domains – Multiple runs of submission
  14. 14. Evalita 2016?

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