REAL - T IME T E X T A NA LY T I C S
Figure 3: Trends stream graph.
Incoming tweets over a time period
were captured in a stream graph visualization as shown in the Figure 3 screenshot. Each topic is represented by a
stream in the visualization and is characterized by the top words in that topic. At
any point of time, the top words in each
topic are displayed in a topic treemap
below the stream graph. It is possible to
get the keyword “treemap” at any past
time in history.
Successive runs of the sentiment
analysis algorithm for batches of tweets
are represented by the visual in Figure 4.
Each bar captures the sentiment
for that feature in a particular batch
of tweets. The height of the bar represents the number of opinion words
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A N A LY T I C S - M A G A Z I N E . O R G
for the feature in that batch. The color of each bar represents the overall
sentiment level expressed in a batch of
data, ranging from extremely negative
(dark red) to extremely positive (dark
green). The change in color of the bars
across various batches can be used
to identify stimuli that are driving the
change.
Selection of a particular bar provides
a deeper analysis of that batch. The size
of a bubble indicates the number of references of a particular opinion word, and
the color shows the overall sentiment
score for the particular opinion word.
Both the size and color are indicators of
which opinion words drive the sentiment
for a feature in a batch.
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