SentiCompass: Interactive Visualization for Exploring and Comparing the Sentiments of Time-Varying Twitter Data

Florence Ying Wang, Arnaud Sallaberry, Mathieu RocheLIRMM & Universite Montpellier, France

Karsten KleinMonash University, Australia

Masahiro Takatsuka The University of Sydney, Australia

14-17 April, Hangzhou, ChinaIEEE Pacific Visualization Symposium 2015

AbstractVisualization 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

IntroductionThe 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.

IntroductionIn 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

Visualization RequirementsIn 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.

Visualization RequirementsNo Requirement

ET1 Visualize the number of tweets and the strength of a sentiment at a given time segment

ET2 Compare the number of tweets and strength for different sentiments at a given time segment

No Requirement

ST1 Visualize both dimensions of sentiments (i.e. valence and arousal) and be able to look up their semantic meanings

ST2 Visualize and be able to compare the distances of different sentiments in affective space

ST3 Visualize and be able to compare the volume of tweets over time

ST4 Visualize the temporal variations of sentiment patterns on one specific topic

ST5 Visualize the dominant sentiments of one specific topic at different time frames

ST6 Compare temporal sentiments variations of different topics at different time frames

ST7 For tweets on one or more topics, find out if any time frames have similar sentiment patterns

Elementary Task (ET)

Synoptic Task (ST)

Hybrid Approach for Sentiment AnalysisFirst, 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.

Hybrid Approach for Sentiment AnalysisSecond 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.

Hybrid Approach for Sentiment Analysis

Illustration

The Visual Metaphor of SentiCompassBased on the requirements, they consider the following four aspects:

1. 2D Psychological Model of Affect2. Cyclic Representation of 2D Sentiment Data3. Perspective Representation of Time Tunnel4. Color Coding

The Visual Metaphor of SentiCompass1. 2D Psychological Model of AffectRussel’s circumplex model is chosen because two principal axises of sentiment (valence and arousal) are most commonly used in testing stimuli of affective words.

The Visual Metaphor of SentiCompass2. Cyclic Representation of 2D Sentiment DataIn order to better discern the cyclic behavior of sentiment data, cyclic representation adopted.For every tweet, first, they calculate its polar coordinates in the affective space using

SentiѲi = arctan(),

Sentiρi = a = arousal scoresv = valence scoresSentiѲi = a specific type of sentimentSentiρi = strength of the sentiment

Then to obtain the cyclic representation, they calculate a circular histogram using the polar coordinates.

The Visual Metaphor of SentiCompass

The Visual Metaphor of SentiCompassFor each bin, they first count the number of tweets falling into the bin using SentiѲi. Assume bin j consists of Nj tweets, then they calculate the mean sentiment strength and standard deviation using

MeanStrengthj =

StdStrengthj =

α ≤ SentiѲi ≤ β

α = start angle of the binβ = end angle of the bin

The Visual Metaphor of SentiCompass

The Visual Metaphor of SentiCompass3. 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).

The Visual Metaphor of SentiCompass

time

Prerspective projection of time rings

Valence

The Visual Metaphor of SentiCompass4. Color CodingIn 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.

The Visual Metaphor of SentiCompass

ConclusionThis 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

Thanks for your attention