Emotion Classification Using Massive Examples Extracted From The Web
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Emotion Classification Using Massive Examples Extracted From The Web

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    Emotion Classification Using Massive Examples Extracted From The Web Emotion Classification Using Massive Examples Extracted From The Web Presentation Transcript

    • Emotion Classification Using Massive Examples Extracted from the Web Ryoko TOKUHISA, Kentaro INUI, Yuji MATSUMOTO COLING’ 2008 Date: 2009-02-19
    • Outline
      • Introduction
      • Emotion Classification
      • Experiments
      • Conclusion
    • Introduction
      • Goal: proposing a data-oriented method for inferring the emotion of a speaker conversing with a dialog system.
      • Method
        • Obtaining a huge collection of emotion-provoking event instances from the web.
        • Decomposing the emotion classification task into two sub-steps:
          • Coarse-grained: sentiment polarity classification .
          • Fine-grained: emotion classification .
    • The Basic Idea
      • Classification problem: a given input sentence is to be classified either into 10 emotion classes or neutral class .
      • Basic idea: learning what emotion is typically provoked in what situation (emotion-provoking event).
        • Ex.: “ I traveled for to get to the shop, but it was closed ” -> disappointing .
    •  
    • Building an EP Corpus
      • Taking ten emotions ( happiness , fear …) as emotion classes.
      • Building a handcrafted lexicon of emotion words (349 emotion words) classified into the ten emotions.
    • Building an EP Corpus cont.
      • Using 349 emotion words to find sentences in the Web corpus that possibly contain emotion-provoking events.
      • A subordinate clause was extracted as an emotion-provoking event instance if:
        • It was subordinated to a matrix clause headed by an emotion word.
        • The relation between the subordinate and matrix clauses is marked by one of the eight connectives ( ので , から , ため , て , のは , のが , ことは , ことが ).
        • Ex.: “ I was disappointed that is suddenly started raining. ”
        • the subordinate: it suddenly started raining .
        • connective: that .
    • Building an EP Corpus cont.
      • Apply above emotion lexicons and patterns to collection 1.3 million events.
      • The evaluation of EP corpus by annotators.
    • Sentiment Polarity Classification
      • Neutral sentences are not the majority in real Web texts.
        • 1000 sentences randomly sampled from the web:
      • Using the positive and negative examples stored in emotion-provoking corpus.
      • Assuming the sentence to be neutral if the output of the model is near the decision boundary.
    • Sentiment Polarity Classification cont.
      • SVMs and the features ( n -grams and the sentiment polarity of the word themselves).
        • where, the sentiment dictionary (1880 positive words and 2490 negative words) from 50 thousand most frequent words sampled from the Web.
    • Emotion Classification
      • Applying the KNN (k-nearest-neighbor) approach by using the EP corpus.
      • Similarity measure: using cosine similarity between bag-of-words vectors ( I nstance and EP )
    • Experiment for Sentiment Classification
      • Two test sets:
        • TestSet1: 31 positive utterances, 34 negative utterances, and 25 neutral utterances.
        • TestSet2: 1140 samples (judged Correct ) are 491 positives, 649 negatives sentences and additional 501 neutral sentences.
      • Testing classification in both two-class and three-class setting.
      • Metric: F-measure
    •  
    • Experiment for Emotion Classification
      • Three test sets
        • TestSet1 (2p, best)
        • TestSet1 (1p, acceptable)
        • TestSet2: using the results of their judgments on the correctness.
    • Baseline vs. KNN
      • Baseline (Pointwise Mutual Information, PMI )
        • where e i ∈ { angry, disgust, fear, joy, sadness, surprise,… }
        • cw j : each content word.
        • Emotion class decision:
      • KNN: 1-NN, 3-NN and 10-NN.
        • One step: retrieve top-k examples from the EP corpus.
        • Two step: retrieve top-k examples from the corresponding sentiment pool.
    •  
    • Conclusion
      • Decomposing the emotion classification task into two sub-steps.
      • Word n -gram features alone are more or less sufficient to classify sentence when a very large amount of training data is available.
      • Two-step classification was effective for fine-grained emotion classification and outperform baseline model.