Movie Reviews Categorization Using Joint Model of Category, Aspect and Sentiment

Shih-Wen Huang and JiaoJiao Song

Introduction: Three Elements of Movie Reviews

Category Aspect Sentiment

ActionAnimation

ThrillerDocumentary

Romance……

ScreenplaySound Effects

ActingDirecting

……

PositiveNeutral

Negative

- 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

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

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

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

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

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