1. Florence Ying Wang, Arnaud Sallaberry, Mathieu Roche LIRMM & Universite Montpellier, France Karsten Klein Monash University, Australia Masahiro Takatsuka The University of Sydney, Australia 14-17 April, Hangzhou, China IEEE Pacific Visualization Symposium 2015

2. Visualization design combines 2D psychology model of affect (i.e. emotion) with a time tunnel representation. They demonstrate the effectiveness of SentiCompass in achieving various tasks related to temporal sentiment and affective analysis of tweets. Interactive demo of the system is available at: http://youtu.be/ZaMF6VNO7tA http://youtu.be/ZaMF6VNO7tA

3. The growth of online social media such as Twitter has facilitated more people to get involved in social events. Therefore, the adequate collection of Twitter data can be used as an important resource for monitoring people’s attitudes on global events such as sport matches or political elections.

4. In 2011, Ramaswamy combined scatter plot, heat map, and tag cloud with circumplex model for visualizing 2D sentiments of tweets using multiple views. However, the temporal information of tweets are not associated with sentiment visualization. In this work, they aim to address the problems that is representations of sentiments and temporal information by proposing a visualization design called SentiCompass

5. In designing the visualization, they targeted data type is textual Twitter data collected over a continuous period of time tracking tweets on specific events. They summarize detailed requirements of visualization using the task model introduced by Andrienko (2006). They divided into elementary and synoptic tasks. Elementary tasks refer to individual data elements, synoptic tasks consider a whole set or subset of data.

6. NoRequirement ET1Visualize the number of tweets and the strength of a sentiment at a given time segment ET2Compare the number of tweets and strength for different sentiments at a given time segment NoRequirement ST1Visualize both dimensions of sentiments (i.e. valence and arousal) and be able to look up their semantic meanings ST2Visualize and be able to compare the distances of different sentiments in affective space ST3Visualize and be able to compare the volume of tweets over time ST4Visualize the temporal variations of sentiment patterns on one specific topic ST5Visualize the dominant sentiments of one specific topic at different time frames ST6Compare temporal sentiments variations of different topics at different time frames ST7For tweets on one or more topics, find out if any time frames have similar sentiment patterns Elementary Task (ET) Synoptic Task (ST)

7. First, they adopt ANEW (Affective Norm for English Words) dictionary to estimate valence (the level of pleasantness) and arousal (the level of activation). ANEW contains affective ratings for 1034 English words obtained from Empirical Studies. Each word, the ratings consist of mean and standard deviation. Then they do statistically weight both valence and arousal ratings of multiple affective words.

8. Second approach, they calculate the polarity (i.e. negative or positive meaning) of the tweet using Naïve Bayes classifier. For each tweet, both valence and arousal dimensions are estimated, and then polar coordinates on affective space are calculated. Which consist of the angle referring to a specific type of sentiment and the radius indicating the strength of sentiment.

9. Illustration

10. Based on the requirements, they consider the following four aspects: 1. 2D Psychological Model of Affect 2. Cyclic Representation of 2D Sentiment Data 3. Perspective Representation of Time Tunnel 4. Color Coding

11. 1.2D Psychological Model of Affect Russel’s circumplex model is chosen because two principal axises of sentiment (valence and arousal) are most commonly used in testing stimuli of affective words.

16. 3. Perspective Representation of Time Tunnel To incorporate the temporal dimension, they represent time as a cylindrical tunnel with different time periods represented by time rings (as figure top view). Then they apply perspective projection to add the time dimension into 2D visualization (as figure perspective view).

17. time Prerspective projection of time rings Valence

18. 4. Color Coding In addition to the cyclic representation of sentiment, they use two pairs of complimentary color to represent two dimensions of sentiment: Valence (green to red, indicating pleasant level) and Arousal (blue to yellow, indicating activation level). Cold colors such as blue, green indicating low level, whereas warm colors such as red, yellow indicate high level.

20. This research combines the circumplex model of affect with the time tunnel representation These two case studies show the effectiveness of SentiCompass in achieving various tasks related to temporal sentiment and affective analysis of tweets