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

Action

Animation

Thriller

Documentary

Romance

……

Screenplay

Sound Effects

Acting

Directing

……

Positive

Neutral

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

𝜃𝑐𝑖,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

24.5%

25.3%

31.1%

33.1%

23.0%

24.5%

22.8%

23.9%

20.0%

22.0%

24.0%

26.0%

28.0%

30.0%

32.0%

34.0%

10 50 100 200

Result of Single Category Label Recovery Task

Joint Model for Single

Category Label

EM

Iterations

Experimental Result

75.1%

75.4%

76.0%

76.6% 76.9%

75.6%

74.8%

74.0%

74.9%

74.2%

72.5%

73.0%

73.5%

74.0%

74.5%

75.0%

75.5%

76.0%

76.5%

77.0%

77.5%

20 40 60 80 100

Result of Multiple Category Labels Recovery Task

Joint Model with Multiple

Category labels

EM

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