1. INTERSUBJECTIVITY AND SENTIMENT: FROM LANGUAGE TO KNOWLEDGE
Lin Gui (Harbin Institute of Technology),
Ruifeng Xu (Harbin Institute of Technology),
Yulan He(Aston University),
Qin Lu (Hong Kong PolyU),
Zhongyu Wei (The University of Texas at Dallas)

2. Outline Background Network Embedding with Intersubjectivity
Sentiment with Intersubjectivity
Experiment

3. Background The history of language
The gap between language and knowledge (Dunbar and Dunbar, 1998)
Subjectivity (river, lion)
Intersubjectivity (nation, country)

4. Background
Intersubjectivity suggests that the meaning of a word or a phrase is not encoded in the surface form of that language as a mapping from a term to an object or a subject.
It is a commonly accepted conceptualization by a society sharing the same language.

5. Our Approach
We proposed an intersubjectivity based sentiment classification method.
1. Construct an intersubjectivity network
2. Learning vertex representation with embedding
3. A CNN based sentiment classification with the embedding result.

6. Construct an intersubjectivity network

7. Construct an intersubjectivity network

8. Learning vertex representation
How to define the conditional probability
Two author vertices share similar subjective terms should have high conditional probability
The objective function is:

9. A CNN based sentiment classification

10. Experiment
The distribution of the data set

11. Experiment The referenced method The metrics:
Paragraph vector for document modeling
Recursive Neural Tensor Network
Convolutional Neural Network
Jointly Modeling Aspects, Ratings and Sentiments
User Product Neural Network
The metrics:

12. Experiment
The performance of referenced method

13. Experiment
The comparison with or without author modeling

14. Experiment
The top 5 most positive/negative author in the embedding result

15. Experiment
The distribution of terms and authors in the embedding space

16. Further Discussion
Bigram or Unigram?
Word embedding?

17. Thanks!