MoodUs - WP7 Apps - Chatterbox+Twitter-API Apps for Windows Phone 7 combining your twitter data and sentiment analysis for interesting mood forecast and statistics.

1. Alfian Akbar Gozali
113060074

2. Background
Growth of the Internet Users
Growth of the internet WebPages
Search engine development
Enormous number of indexing terms

3. Solution: Document Extraction!!
Hidden Markov Model (HMM)
Documents Extraction

4. The Goals are Analyze…
The HMM works
Effects of the parameter in HMM
Differences between ordinary indexing and
compression indexing with HMM
Effects of document variation to this system

5. What is HMM?
One of the Markov Chain
enhancement
Predict the sequence of pattern
that can’t be observed directly
Consist of two state trails:
observed and hidden

6. HMM Elements

7. Topic Sentence Extraction
Depends on particular language
Doesn’t depend on particular language
• Statistical approach
• HMM Hedge

8. ROUGE - 2
 Measure accuracy between human extraction and
system extraction

9. Overall Design

10. System Testing
{NAME} and {NUMERIC} tag
α parameter in decoding
effect of extraction
corpus kinds

11. Result – Scenario 1 (tagging)
pEmiss
4000
3000
terms 2000
1000 Tagging
0 Untagging
0 100 200
Number of docs
Evaluation Extracting
250 4000
200 3000
150
time time 2000
100 Tagging Tagging
50 1000
Untagging Untagging
0 0
0 50 100 150 200 0 50 100 150 200
Number of documents number of documents

12. Result – Scenario 2 (alpha)

13. Result – Scenario 3 (extraction)
Execution Time
dengan kompresi
71.95%
tanpa kompresi
Number of Terms
dengan kompresi
56.98% tanpa kompresi

14. Result – Scenario 4 (corpus)
Extraction Accuracy
80%
60%
40% min
akurasi
20% maks
0% akurasi
rerata
rerata
maks akurasi
min akurasi

15. Conclusion
Tagging can reduce extracting time
and number of indexed terms
Optimum alpha parameter is 0,2 and
0,3
Compression can reduce indexing
time and number of indexed terms
Variation of the corpuses can
influence system accuracy

16. That’s all…