movie review categorization using joint model
DESCRIPTION
TRANSCRIPT
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Movie Reviews Categorization Using Joint Model of Category, Aspect and Sentiment
Shih-Wen Huang and JiaoJiao Song
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Introduction: Three Elements of Movie Reviews
Category Aspect Sentiment
ActionAnimation
ThrillerDocumentary
Romance……
ScreenplaySound Effects
ActingDirecting
……
PositiveNeutral
Negative
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- For each document d - choose a category label c- For each word w in document d - choose an aspect label ai from ϕc
- choose a sentiment label si from πa,d
- choose a word wi from
Two Generative Models
Joint Model with Single Category label
𝜃𝑎𝑖 , 𝑠𝑖
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Two Generative Models
Joint Model with Multiple Category labels
- For each document d - choose a category distribution ηd
- choose a sentiment distribution πd
- For each word w in document d - choose a category label ci from ηd
- choose an aspect label ai from ϕc
- choose a sentiment label si from πd
- choose a word wi from 𝜃𝑐 𝑖 ,𝑎𝑖 , 𝑠𝑖
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Experimental Design
• 1657 movie reviews parsed from Epinions.com.
• The reviews are in 7 categories. (action, romance, etc.) Some of the reviews are associated with multiple categories
• Single Category Label Recovery Task and Multiple Category Labels Recovery Task
• The proposed models were compared with EM
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Experimental Result
10 50 100 20020.0%
22.0%
24.0%
26.0%
28.0%
30.0%
32.0%
34.0%
24.5%25.3%
31.1%
33.1%
23.0%24.5%
22.8%23.9%
Result of Single Category Label Recovery Task
Joint Model for Single Category LabelEM
Iterations
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Experimental Result
20 40 60 80 10072.5%
73.0%
73.5%
74.0%
74.5%
75.0%
75.5%
76.0%
76.5%
77.0%
77.5%
75.1%
75.4%76.0%
76.6%76.9%
75.6%
74.8%
74.0%
74.9%
74.2%
Result of Multiple Category Labels Recovery Task
Joint Model with Multiple Category labelsEM
Iterations
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Conclusion
• The proposed joint models outperform EM model in category label recovery task
• It is also possible to compute the similarity between documents based on the category and aspect distribution