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A cognitive study of subjectivity extraction in sentiment annotation

A cognitive study of subjectivity extraction in sentiment annotation. Abhijit Mishra 1 , Aditya Joshi 1,2,3 , Pushpak Bhattacharyya 1 1 IIT Bombay, India 2 Monash University, Australia 3 IITB-Monash Research Academy.

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A cognitive study of subjectivity extraction in sentiment annotation

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  1. A cognitive study of subjectivity extraction in sentiment annotation Abhijit Mishra1, Aditya Joshi1,2,3, Pushpak Bhattacharyya1 1 IIT Bombay, India 2 Monash University, Australia 3IITB-Monash Research Academy At 5th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, ACL 2014, Baltimore

  2. Subjectivity Extraction • Goal: To identify subjective portions of text

  3. Motivation • Strong AI suggests that a machine must be perform sentiment analysis in a manner and accuracy similar to human beings • Do humans perform subjective extraction as well? A “cognitive study” of subjectivity extraction in sentiment annotation

  4. Outline • Sentiment Oscillations & Subjectivity Extraction • Experiment Setup • Anticipation & Homing • Conclusion & Future Work

  5. Sentiment Oscillations & subjectivity extraction • Subjective documents may be: • Humans perform subjectivity extraction either as a result of “anticipation” or as “homing”. • Which of the two methods are adopted depends on the linear/oscillating nature of the subjective document. Linear: The story was captivating. The actors did a great job. I absolutely loved the movie! Oscillating: The story was captivating. Only if they had better actors. But then I enjoyed the movie, on the whole.

  6. Experiment Setup (1/2) • A human annotator reads a document and predicts its sentiment • A Tobii T120 eye-tracker records eye movements while he/she reads the document * No time restriction, no user input required: to minimize errors.

  7. Experiment Setup (2/2) • Dataset • 3 Movie reviews in English from imdb • One linear, one oscillating, one between the two extremes (D0, D1, D2 respectively) • Three documents? Really?! • To eliminate predictability • To reduce errors due to fatigue • 12 human annotators (P0, .. P11 respectively)

  8. Observations: Anticipation (1/2) • In case of linear subjective documents, an annotator reads some sentences and begins to skip sentences.

  9. Observations: Anticipation (2/2)

  10. Observations: Homing (1/3) • In case of oscillating subjective documents, an annotator (a) first reads all sentences, (b) revisits some sentences again

  11. Observations: Homing (2/3) • Considerable overlap between sentences that are read in the second pass • All of them are subjective. Reading statistics for D1 TFD: Total fixation duration for subjective extract; PTFD: Proportion of total fixation duration = (TFD)/(Total duration); TFC-SE: Total fixation count for subjective extract

  12. Observations: Homing (3/3) • Homing at a sub-sentence level • Sarcasm • Multiple regressions around the sarcasm portion for participant P1, document D1 • Participant P1 does not correctly detect the sentiment of the document • Thwarting

  13. Conclusion & Future Work • Based on how sentiment changes through a document, humans may perform subjectivity extraction as a result of anticipation or homing • Applications: • Pricing models for crowd-sourced annotation • Sentiment classifiers that incorporate “sentiment runlengths”

  14. References • WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia, SubhabrataMukherjee and Pushpak Bhattacharyya, ECML PKDD 2012 • A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts, Bo Pang, Lillian Lee, ACL 2004

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