文献紹介:an empirical study on the effect of negation words on sentiment
TRANSCRIPT
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文献紹介2014/09/09
長岡技術科学大学自然言語処理研究室
岡田正平
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文献情報Xiaodan Zhu, Hongyu Guo, Saif Mohammad and Svetlana KiritchenkoAn Empirical Study on the Effect of Negation Words on Sentiment In Proceedings of the 52nd ACL, pp 304-313.2014.
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概要• 感情解析における否定語の振舞いに関する定量的な調査
– 否定語:no, not 等• これまで用いられてきたヒューリスティクス
– 否定語の効果は修飾する句の極性のみに依存する否定語の振舞いを充分に扱えていない
• 否定語や修飾する句に依存させることで性能改善
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背景
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背景• 感情解析において否定語が与える影響の理解は不可欠• 単純な仮説
– 否定語は修飾するテキストの極性を反転させる(Polanyi and Zaenen, 2004; Kennedy and Inkpen, 2006)– sentiment value を一定値シフトさせるshifting hypothesis (Taboada et al., 2011)
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背景• Stanford Sentiment Treebank (Socher et al., 2013)
– 構文木中の全 phrase に sentiment score を人手でアノテーション
– 感情解析における初の完全なラベル付き構文木– 11,855文,215,154 unique phrases– http://nlp.stanford.edu/sentiment
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背景Stanford Sentiment Treebankにおける否定語の振舞い
横軸:被修飾句 (argument)のsentiment value
縦軸:否定語+被修飾句(negated phrase) のsentiment value
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背景• 極性の反転は,振舞いの一部のみしか捉えていない
– reversing hypothesisは不十分• 被修飾句自体は同じ sentiment value をとっても否定語を伴うことで異なる sentiment value – shifting hypothesis でも不十分
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背景• この論文では...
– 既存のヒューリスティクスの性能評価– 既存のヒューリスティクスの拡張・評価– state-of-the-artな手法の性能評価– state-of-the-artな手法の拡張・評価
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Negation models
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Negation models• 𝑤 : argument• 𝑤𝑛 : negator• 𝑠(𝑤) : argument の sentiment score• 𝑠(𝑤𝑛,𝑤) : negated phrase の sentiment score
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Models based on heuristics• Non-lexicalized assumptions and modeling
– Reversing hypothesis– Shifting hypothesis
• Basic shifting• Polarity-based shifting
• Simple lexicalized assumption– Negator-based shifting– Combined shifting
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Models based on heuristics• Non-lexicalized assumptions
– Reversing hypothesis– Shifting hypothesis
• Basic shifting• Polarity-based shifting
• Simple lexicalized assumption– Negator-based shifting– Combined shifting
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Non-lexicalized assumption• 既存の手法のモデル
𝑠 𝑤𝑛,𝑤 ≝ 𝑓(𝑠 𝑤 )
• argument の sentiment score のみに依存するモデル
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Non-lexicalized assumption• Reversing hypothesis
𝑓 𝑠 𝑤 = −𝑠(𝑤)• Shifting hypothesis
– Basic shifting𝑓 𝑠 𝑤 = 𝑠 𝑤 − 𝑠𝑠𝑠𝑠 𝑠 𝑤 ∗ 𝐶
– Polarity-based shifting𝑓 𝑠 𝑤 = 𝑠 𝑤 − 𝑠𝑠𝑠𝑠 𝑠 𝑤 ∗ 𝐶(𝑠𝑠𝑠𝑠 𝑠 𝑤 )
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Non-lexicalized assumption• Reversing hypothesis
𝑓 𝑠 𝑤 = −𝑠(𝑤)• Shifting hypothesis
– Basic shifting𝑓 𝑠 𝑤 = 𝑠 𝑤 − 𝑠𝑠𝑠𝑠 𝑠 𝑤 ∗ 𝐶
– Polarity-based shifting𝑓 𝑠 𝑤 = 𝑠 𝑤 − 𝑠𝑠𝑠𝑠 𝑠 𝑤 ∗ 𝐶(𝑠𝑠𝑠𝑠 𝑠 𝑤 )
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Models based on heuristics• Non-lexicalized assumptions and modeling
– Reversing hypothesis– Shifting hypothesis
• Basic shifting• Polarity-based shifting
• Simple lexicalized assumption– Negator-based shifting– Combined shifting
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Simple lexicalized assumption• non-lexicalized model を拡張
𝑠 𝑤𝑛,𝑤 ≝ 𝑓(𝑤𝑛, 𝑠 𝑤 )
• nagatorにも依存するモデル
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Simple lexicalized assumption• Nagator-based shifting
𝑓 𝑤𝑛, 𝑠 𝑤 = 𝑠 𝑤 − 𝑠𝑠𝑠𝑠 𝑠 𝑤 ∗ 𝐶(𝑤𝑛)
• Combined shifting𝑓 𝑤𝑛, 𝑠 𝑤 = 𝑠 𝑤 − 𝑠𝑠𝑠𝑠 𝑠 𝑤 ∗ 𝐶(𝑤𝑛, 𝑠𝑠𝑠𝑠 𝑠 𝑤 )
• パラメータ数は増加するが過学習を起こさずに性能向上
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Semantics-enriched modeling• argument 自身に依存するモデル
𝑠 𝑤𝑛,𝑤 ≝ 𝑓 𝑤𝑛, 𝑠 𝑤 , 𝑟 𝑤
• 𝑟(𝑤): argument 𝑤 の何かしらの表現
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Semantics-enriched modeling• 𝑟(𝑤)をどう実装するか?
– recursive neural tensor network (RNTN)• Socher et al. (2013), state-of-the-artな性能を達成• 𝑤𝑛と𝑟(𝑤)のみを利用
– prior sentiment-enriched tensor network (PSTN)• 提案手法• 𝑤𝑛, 𝑠 𝑤 , 𝑟(𝑤)を利用
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Semantics-enriched modeling
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Semantics-enriched modeling• RNTN
– 各ノードは𝑑次ベクトル– 各ベクトルは2つの子ノードから非線形関数によって求められる
𝑝2 = tanh (𝑎𝑝1
𝑇
𝑉 1:𝑑 𝑎𝑝1
+ 𝑊𝑎𝑝1
)
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Semantics-enriched modeling • PSTN
– 各ベクトルは2つの子ノードと,右側の子ノード (=argument) の感情から求められる
𝑝2 = tanh (𝑎𝑝1
𝑇
𝑉 1:𝑑 𝑎𝑝1
+ 𝑊𝑎𝑝1
+𝑎
𝑝1𝑠𝑠𝑛𝑇
𝑉𝑠𝑠𝑛 1:𝑑 𝑎𝑝1𝑠𝑠𝑛
+ 𝑊𝑠𝑠𝑛 𝑎𝑝1𝑠𝑠𝑛
)
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Semantics-enriched modeling• 𝑝1𝑠𝑠𝑛には様々な形で感情の値をとることができる
– PSTNの面白いところ• 本論文の目的は否定語の振舞いを調査すること
– 𝑝1𝑠𝑠𝑛には人手で付けられたタグを用いる
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実験
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実験 | set-up• Stanford Sentiment Treebank中の否定語+被修飾句全2,261 組がデータ点
• 頻出する否定語と,助動詞+否定語の組み合わせが対象– 同じ否定語は正規化する e.g.) isn’t, is not → is_not
• 評価には平均絶対値誤差𝑀𝑀𝑀 =
1𝑁
� �̂� 𝑤𝑛,𝑤 − 𝑠(𝑤𝑛,𝑤)𝑤𝑛,𝑤
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実験 | 結果
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考察
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考察• 各否定語の sentiment
value の遷移量(boxは95%信頼区間)
• nagator-based shiftingの有効性
• 否定語とdinimisher(図中の白いboxのもの)の境界は曖昧
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考察• argument の極性による否定語の振舞いの違い
• polarity-based shiftingの有効性
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References
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References• Livia Polanyi and Annie Zaenen. Contextual valence
shifters. In Exploring Attitude and Affect in Text: Theories and Applications (AAAI Spring Symposium Series). 2004.
• Alistair Kennedy and Diana Inkpen. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2):110-125. 2006.
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References• Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly
Vol, and Manfred Stede. Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2):267-307. 2011.
• Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. Recursive deep modwls for semantic compositionality over a sentiment treebank. In Proc. of EMNLP’13. 2013.
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