Support for Understanding Others by Visualizing
Sentiment of Social Media Posts
Harumi Murakami

Reo Saitoh

Graduate School of Informatics
Osaka Metropolitan University
Osaka, Japan
harumi@omu.ac.jp

Graduate School of Engineering
Osaka City University
Osaka, Japan
reo.1016.02@gmail.com

Abstract—Understanding people is critical in various aspectsof our daily lives. We believe that extracting and visualizing
the emotions included in social media posts will facilitate theunderstanding of people. Based on this idea, we developed a toolfor understanding others using sentiment analysis of a user’s
Twitter and Instagram posts. We experimentally evaluated ourapproach and the tool to understand professional baseball players
as examples of others.
Index Terms—Twitter, Instagram, emotion, sentiment analysis

I. I NTRODUCTION
Understanding people is important in such aspects asdecision-making and planning in our daily lives. Although
doing an internet search is useful to learn more about people, the information provided by such searches is generally
superficial, including occupation and organization. We wantmore information about what he/she likes and what he/shedoes both at work and during his/her private times. To obtainsuch personal information about people, we turn to their SNS
posts, which often mention activities and describe them withemotions. We believe that extracting and visualizing emotions
included in such posts will increase our understanding ofpeople. Based on this idea, we developed a tool for selfunderstanding using sentiment analysis of a user’s Twitter
posts [1]. In this paper, we extend this idea and develop atool for understanding others using sentiment analysis of bothTwitter and Instagram posts.
Below we explain our approach’s overview in Section 2 anddescribe our new tool for understanding others and report theexperimental results in Section 3.
II. A PPROACH
We visualize the emotions included in user social media
posts using the Google Natural Language API sentiment
analysis to get a score and a magnitude for each post. We
display the following: several types of bar graphs that show
the transition of emotions, a list of posts with emotional scores,a pie chart showing the proportion of emotions, and the wordlists included in positive and negative posts.
We developed two bar graphs with different units: posts
and periods (day and month). A bar graph by post displays
post scores. We summarize the period scores by developing

three bar graphs with different values: A) maximal valuesof the scores (both positive and negative); B) their averagevalues; and C) a modified average value using the scores andmagnitudes. A score varies from -1 to 1, and a score less than
0 is negative, 0 is neutral, and greater than 0 is positive.
Graphs A and B have individual advantages and disadvantages. Graph A emphasizes the highest valued posts, although
it hides other posts in the designated period. Graph B summarizes the values in one value, although it offsets the plusses
and minuses and the values tend to be small, complicating thedetection of posts with high emotion content. To overcome
the disadvantage of graph B, we designed graph C, whichemphasizes stronger emotions with a modified average value(an E-score):( PnE-score =|Scorei ×M agnitudei |i=1PnPn i=1 M agnitudei|Scorei ×M agnitudei |i=1P−ni=1 M agnitudei

ifPni=1 Scorei ≥ 0,otherwise,

where Scorei is a Google score for post pi , M agnitudei is
its magnitude, and n is the number of posts except for those
whose scores are 0.
Posts can be sorted by date or by score in a list of posts.
A pie chart shows the ratio of the positive/negative/neutralscores of the posts. Our tool displays the top five frequent
word lists extracted from the positive and negative posts. First,
we performed morphological analysis and assigned the scoreof one post to the words contained in it. For each positive
and negative category, the summation of the word’s score isdivided by its frequency.
We developed a tool for understanding others by modifying
our previous tool [1]. The main difference is that we added
Instagram posts to Twitter posts. Fig. 1 shows an example fora professional baseball player. After selecting a person, a user
inputs the periods (one month in this example) and selects“Per day: graph A” and “Mixed,” bar graph A, a log list, anda pie chart; positive/negative word lists are displayed using
both Twitter and Instagram posts.
III. E XPERIMENTS
We experimentally evaluated our approach and the tool. As
examples of “others,” we selected 51 Japanese professional

C. Experiment 3: Tool
We evaluated our tool utility. Our subjects used it for aplayer, described his most positive and negative events forthree months, and answered the questions in Table I. The
average values were equal to or over 4.0, except for Q1, Q3,and Q4. Although the bar graphs and the log lists were useful,the words were less useful.
TABLE IE XPERIMENTAL R ESULTS FOR THE T OOL

Fig. 1. Screen example

baseball players who have both Twitter and Instagram accounts. Our ten subjects (nine Japanese males and one female,ages 22-24) used the tool and answered questions about theirexperiences with it.
A. Experiment 1: Google Scores
We verified the validity of the Google scores for the postsby randomly selecting one post from one player. The subjects
evaluated the emotions of the 51 player’s posts by five values(1: very negative; 5: very positive). Two types of correlation
analysis (Kendall and Spearman) were performed between
the Google and manual scores. The average values exceeded
0.3 in both types of analysis (0.349 in Kendall and 0.416 inSpearman). We believe Google scores for posts are promising.
B. Experiment 2: Bar Graphs
We next evaluated the usefulness of the daily and monthly
bar graphs. Our subjects answered the following three questions that evaluated the graphs of one month for daily bar
graphs and those of six months for monthly bar graphs. Q1:“Which graph was useful for understanding his emotions: A,B, or C?” Q2: “Which graph was useful for understanding
his emotions: Twitter only, Instagram only, or mixed?” Q3:“Which graph was the most useful for understanding hisemotions among the nine (combination of 3 (A, B, C) × 3(Twitter only, Instagram only, mixed)) graphs?
For Q1, all the subjects ranked graph A the best for both thedaily and monthly graphs. For the daily graphs, nine subjects
ranked C second, and for the monthly graphs, seven subjects
ranked C second. For Q2, all subjects ranked the mixed graphthe best for both the daily and monthly graphs. Six subjects
ranked Twitter second in both daily and monthly graphs. ForQ3, all the subjects answered that graph-A-mixed was the bestin both the daily and monthly graphs.
We identified the daily and monthly usefulness of bar graph
A. Since graph C is superior to graph B, it compensates
for the latter’s disadvantage. These results are identical toour previous research. We also found that mixed graphs aresuperior and that a graph-A-mixed was best among the ninegraphs because it provides more information than the othergraphs.

Q1
Q2Q3
Q4Q5Q6Q7

Questions
Was the bar graph useful for understanding him?
Was the log useful for understanding him?
Were the words useful for understanding him?
Was the pie chart useful for understanding him?
Was the system useful for understanding him?
Was using the system fun?
Do you want to use the system in the future?

Mean
3.90
4.40
2.70
3.10
4.00
4.00
4.00

SD
0.54
0.49
0.64
0.70
0.45
0.45
0.45

We believe that our approach and tool are promising forunderstanding others. Instagram is another useful source aswell as Twitter for understanding others.
IV. R ELATED W ORK
Numerous studies have addressed sentiment analysis onsocial media, most of which are analyses of user groups orcommunities, not individuals. One exception, Kumamoto etal. [2], proposed a web application system for visualizing
Twitter users based on the temporal changes in impressions
from tweets. We used not only Twitter but also Instagram posts
and summarized and displayed the emotion scores containedin multiple posts. Several articles (e.g., [3] and [4]) surveyed
sentiment analysis on social media and discussed fields relatedto it without addressing our task: understanding people.
The following are the three main contributions of this paper:(1) We presented an approach to support the understanding ofothers; (2) we developed a prototype tool using Twitter andInstagram posts; (3) we evaluated its usefulness.
V. S UMMARY
We developed a tool for understanding others using sentiment analysis of a user’s Twitter and Instagram posts. We
evaluated its usefulness for understanding professional baseball players as examples of others.
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sentiment analysis methods,” ACM Computing Surveys, vol. 49, no. 2,
2016.
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