This slide was presented at the 2016 TAAI conference for Technical Session.

1. Automatic Opinion Leader
Recognition in Group
Discussions
Yu-Chang Ho1, Hao-Min Liu1, Hui-Hsin Hsu2, Sachit Mahajan1,
Chun-Han Lin2, Yao-Hua Ho2, and Ling-Jyh Chen1
Academia Sinica1
National Taiwan Normal University2
The 2016 TAAI Conference
2016.11.26

2. OUTLINE
• Introduction
• Related Work
• System Design
• Evaluation
• Conclusion
AbstractINTRODUCTION 1 / 22

3. INTRODUCTION
• In the era of information explosion, most of the
information seem to be neglected due to the lack of
efficient influence on the receivers.
• There are many ways to promote a product or service.
• Word-of-mouth (WOM) dissemination has stronger
influence on consumer decisions than advertising. [1]
• People often seek for comments.
AbstractINTRODUCTION 2 / 22

4. INTRODUCTION (cont.)
• In word-of-mouth communication, the person who has
the highest influence among others is called the
opinion leader. [2]
• An opinion leader, with his/her ideas and power of oral
communication skills, often becomes an important
reference for a person while making a decision.
• To identify an opinion leader becomes an essential
topic since the opinion leader offers the highest effect
and efficiency of product or service promotion on
marketing. [3-5]
Opinion LeaderINTRODUCTION 3 / 22

5. INTRODUCTION - Contributions
• The contribution of this paper is three-fold:
1. We have proposed a novel approach to conduct
the opinion leader identification by using features
of speech signals.
2. We have proposed 3 algorithms for opinion leader
identification.
3. We have proposed automatic opinion leader
recognizing system, which is composed of a simple
and efficient model to do the opinion leader
identification.
Contributions of this WorkINTRODUCTION 4 / 22

6. RELATED WORK
• “Algorithm of identifying opinion leaders in BBS” [6]
- Analyze the text to do the identification
- PageRank Algorithm
• “Understanding opinion leaders in bulletin board systems:
Structures and algorithms” [7]
- First, analyze the characteristic of opinion leader
- Then, analyze the emotion through the emotion mining methods
• “Research on Methods to Identify the Opinion Leaders in
Internet Community” [8]
- Analyze the influence and the emotion express of the content
- PageRank Algorithm
Other StudiesRELATED WORK 5 / 22

7. SYSTEM DESIGN - Flowchart
• The proposed system flowchart:
The System FlowchartSYSTEM DESIGN 6 / 22
Speech Data Input
Emotion Ratio (E)
Measurement
Conversation Ratio (C)
Measurement
Score of Influential Capacity (S)
Measurement
Opinion Leader
Identification

8. SYSTEM DESIGN – Conversation Ratio
• Conversation Ratio (C) Measurement
- The time spoken by each person in the discussion
Conversation Ratio (C)SYSTEM DESIGN 7 / 22
Parameters Description
L the total discussion time
Ni
the number of times spoken by the i-th person
during total discussion time
λi the speaking frequency of the i-th person
ti,j
the time spent by the i-th person in his j-th
speaking
Ti
the average of each speaking time spent by the i-
th person
Ci the Conversation Ratio of the i-th person

9. SYSTEM DESIGN – Conversation Ratio (cont.)
• Conversation Ratio (C) Measurement
- The Equations:
𝜆" =
𝑁"
𝐿
𝑇" =
∑ 𝑡",*
+,
*-.
𝑁"
𝐶" = 𝜆" ∗ 𝑇"
where 𝐶" ranges from 0 to 1
Conversation Ratio (C)SYSTEM DESIGN 8 / 22

10. SYSTEM DESIGN – Emotion Ratio
• Emotion Ratio (E) Measurement
- The emotion of each member during their speaking in group
discussion
- The Equation:
𝐸" =
∑ 𝑒",*
3,
*-.
𝑀"
Emotion Ratio (E)SYSTEM DESIGN 9 / 22
Parameters Description
Mi
the total number of sentences the i-th person
speaks
ei,j
the result of the emotion recognition on the j-th
sentence spoken by the i-th person
Ei the Emotion Ratio of the i-th person

11. SYSTEM DESIGN – Score of Influential Capacity
• Score of Influential Capacity (S) Measurement
- The Equation:
𝑆" = 𝐶" ∗ 𝐸" + 1
- The value of C and E are positively proportional; therefore,
we adopt multiplication to compute the score (S).
- To avoid an extreme case, E is increased by 1.
Score of Influential Capacity (S)SYSTEM DESIGN 10 / 22

12. EVALUATION - Overview
• Model Training:
We adopted a single dataset using Berlin Database of
Emotional Speech to build the model.
• Model Testing:
We cut the selected YouTube videos into sentences and used
these data as multiple datasets to test our model.
Single
Dataset
Feature
Extraction
Support Vector
Machine (SVM)
Multiple
Datasets
Result
Feature
Extraction
Support Vector
Machine (SVM)
Trained Model
Training
Emotion
Classification
OverviewEVALUATION 11 / 22

13. EVALUATION – Emotion Recognition
• Emotions Classification:
• Feature Extraction: [9]
- We adopted the openSMILE library to do the feature
extraction.
- We chose Energy, Pitch, and Mel-scale Frequency Cepstral
Coefficients (MFCC) of the speech signal to do the analysis.
- We used the support vector machine (SVM) to classify the
speech data.
Emotion RecognitionEVALUATION 12 / 22
Neutral Speech Emotional Speech
The speech w/o any emotion
The speech w/ emotion,
No matter what kind of emotion
the speech carries
emotion recognition result (e) = 0 emotion recognition result (e) = 1

14. EVALUATION – Single Dataset
• Single Dataset Testing Result
– Dataset: Berlin Database of Emotional Speech
*N: Neutral, E: Emotional
Since both Model 1 and Model 3 got the same highest overall accuracy,
we further test them by the multiple datasets.
Single DatasetEVALUATION 13 / 22
Model 1 Model 2 Model 3 Model 4
Features MFCC MFCC + Energy MFCC + Pitch MFCC + Energy + Pitch
Labels* N E N E N E N E
Recall 78 % 98 % 75 % 98 % 76 % 98 % 70 % 98 %
Precision 88 % 95 % 90 % 94 % 90 % 94 % 89 % 93 %
F-measure 82 % 96 % 81 % 95 % 82 % 95 % 78 % 95 %
Overall
Accuracy
94.68 % 94.41 % 94.68 % 93.61 %

15. EVALUATION – Multiple Datasets
• Multiple Datasets Test
̶ We chose 10 YouTube Videos to be our testing data.
̶ The language spoken in these videos includes German, Chinese,
and English
̶ The content includes the fragments of a comedy, quarrel, or
sadness.
̶ Normalization:
𝑉9
=
𝑉3:;
9
∗ 𝑉 − 𝑉=">
𝑉3:; − 𝑉=">
, where 𝑉3:;
9
denotes the maximum number designed by us
to fit the range of SVM input value.
The original feature value ranges from 𝑉="> to 𝑉3:;.
After the normalization, the range would be 𝑉=">
9
to 𝑉3:;
9
.
Multiple DatasetsEVALUATION 14 / 22

16. EVALUATION – Multiple Datasets (cont.)
• Multiple Datasets Testing Result
̶ Since Model 3 got the better result, we use Model 3 to do
the field experiment.
Multiple DatasetsEVALUATION 15 / 22
Model 1 Model 3
Neutral 41 % 55 %
Emotional 76 % 89 %
Accuracy 62 % 76 %

17. EVALUATION – Field Experiment
• The field experiment contains 10 groups with 5
members in each group.
• These 10 groups are constituted with 8 men and 8
women.
• 10 different topics and different discussion time are set
in each group.
• 3 observers are assigned to observe the 5 members
while discussing.
• The observer would give the ranking, from Rank 1 to
Rank 5.
Field ExperimentEVALUATION 16 / 22

18. EVALUATION – Field Experiment (cont.)
• Consistency among rankings by 3 observers in the field
experiment
Observer ConsistencyEVALUATION 17 / 22
91%

19. EVALUATION – Field Experiment (cont.)
• Experimental Result (Heat Map)
Comparison (C, E)EVALUATION 18 / 22
Comparison between the rankings
using Conversation Ratio (C)
Comparison between the rankings using
Emotion Ratio (E)
𝑥 𝑥
𝑦 𝑦
18
19
16
14
20
11
7
11
10
9
18 11

20. EVALUATION – Field Experiment (cont.)
• Experimental Result
Comparison (S)EVALUATION 19 / 22
Comparison between the rankings using
the Score of Influential Capacity (S)
22

21. EVALUATION – Field Experiment (cont.)
• Experimental Result
Cumulative Distribution FunctionEVALUATION 20 / 22
CDF of conversation ratio (C) and emotion ratio (E)
in the field experiment
> 90%
35%
35%
~ 100%
50%
Comparison using Emotion Ratio (E) only

22. EVALUATION – Field Experiment (cont.)
• The results of accuracy measurement:
• Combining the 2 factors will have the better overall
result, which is 73 % accuracy on Rank 1.
Experimental ResultEVALUATION 21 / 22
Rank 1 Rank 2 Rank 3 Rank 4 Rank 5
𝐶" 60 % 58 % 53 % 52 % 67 %
𝐸" 33 % 26 % 26 % 28 % 75 %
𝑆" 73 % 55 % 47 % 63 % 73 %
22
16
14
19
22
22 / 30 16 / 30 14 / 30 19 / 30 22 / 30
Comparison using
the Score of Influential
Capacity (S)

23. CONCLUSION
• In this paper, a novel approach is proposed to identify
the opinion leader in a group discussion by evaluating
the degree of participation and the emotion expression
of the speech for each person.
• Through the field experiment, we proved our approach
have a 73 % accuracy to achieve our goal.
• Our method is simple, efficient and effective.
ConclusionCONCLUSION 22 / 22

24. REFERENCES
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3. C. W. King and J. O. Summers, “Overlap of opinion leadership across consumer product categories,” Journal
of Marketing Research, pp. 43–50, 1970.
4. J. R. Mancuso, “Why not create opinion leaders for new product introductions?” The Journal of Marketing,
pp. 20–25, 1969.
5. R. Dabarera, K. Premaratne, M. N. Murthi, and D. Sarkar, “Consensus in the presence of multiple opin- ion
leaders: Effect of bounded confidence.”
6. Fusuijing Cheng, Chenghui Yan, Yongfeng Huang, Linna Zhou, “Algorithm of identifying opinion leaders in
BBS,” 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems ,Vol. 3, pp.
1149-1152
7. Yu Xiao,Lin Xia, “Understanding opinion leaders in bulletin board systems: Structures and algorithms,”Local
Computer Networks (LCN), 2010 IEEE 35th Conference on
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Opinion Leaders in Internet Community,” Software Engineering and Service Science (ICSESS), 2013 4th IEEE
International Conference on, pp. 934-937.
9. Y. Pan, P. Shen, and L. Shen, “Speech emotion recogni- tion using support vector machine,” International
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REFERENCES

25. Thank you for your listening!
THE END