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Twitter
Search,
Social Mention, Twendz, Twitter
Sentiment, and Twitrratr
by Chris
Near, Director of Research, KDPaine & Partners
Don't
miss Chris Near's other
article on measuring Twitter, also in this issue of The
Measurement Standard: "Which Twitter Profile Analysis Tool Rules the Nest?" The
sentiment analysis tools in the article
on this page look specifically at the tone of individual tweets,
while the Twitter analysis tools of the
other article rank a person's entire Twitter profile
in terms of popularity and engagement.
There
is big, big, big demand for the ability to measure sentiment in social
media. Sentiment -- also called
"tone," and typically rated as positive, negative or neutral
-- is shorthand for what the world thinks
about something. And everyone, from junior high kids ("Do
they like me or not?") to billion-dollar corporations
("Do they like our latest product?"), wants
to know what the world thinks of them.
So, now
that Twitter rules the social media roost, it would
be very,
very handy to have an accurate sentiment
analysis
tool that could quickly and easily gauge the tone
of
Twitter conversations.
When my
company, KDPaine & Partners, wants to do very accurate measurement
of Twitter, we have to use human readers. It's the only way to really
understand the language. But, to
have human eyes read and rate every tweet often takes
a lot of time and money. For fast and free measurement, there
are automated online sentiment analysis tools, five of which I review
below. They, however, have their own set of problems.
Yeah,
right. I got your total accuracy right here!
For
a computer to determine sentiment for
traditional print
media is difficult
enough.
But when you try to get a computer to
understand
and interpret social media, it gets infinitely harder.
Social
media, Twitter especially, is a very casual format, one that lends
itself
to inside jokes, symbols, emoticons, abbreviations, and jargon.
Oh, and don't forget about sarcasm;
what might appear positive might
really
be negative,
and vice versa. Add to that the fact that Twitter only allows
140 characters (fewer characters
and words makes it more difficult to assess tone) and errors
in automated analysis go way up. So we may never attain total accuracy
in measuring sentiment for Twitter. (Even with human readers you
often don't get 100% accuracy.)
Most automated
Twitter sentiment analyzers are very up front
about the difficulty
of what they are trying to do, and that they will be changing how
they do it in the future. Before we get to the reviews,
let's think about...
The Ideal
Twitter Sentiment Tool
To my
way of thinking, and for the type of work we typically do at KDPaine
& Partners, the perfect Twitter
sentiment analysis tool would include the following features:
- The
ability to search by any word, abbreviation, symbol, or emoticon
that would ever occur in a tweet.
- Advanced
search features, including by date, by user, by hashtag, and
by tone.
- The
ability to
distinguish between posts from a person/company, posts to a
person/company, and posts referencing a person/company.
- Results charts
with colors that distinguish between different sentiments.
- A dashboard
with quick totals for each tone type, as well as
overall numbers/percentages.
- The ability
to search over long periods of time.
- Instant
charts to show trends and
relevant occurrences.
- At
least 80% accuracy. 90% - 95% would be better.
- Real
time updates.
- It
should be free. (That's not
too much to ask, is it?)
None
of the tools reviewed below come even close to meeting this wish
list. But some are on the right track. Here they are:
Twitter
Search
Twitter's own Twitter Search sentiment option does not use words to
gauge message tone, rather, it is limited to only those tweets
that include
certain characters used to symbolize mood, e.g., :) or :(
or
:D or :P. For
this reason, when you use Twitter Search to search any given
topic, you may find only one or two results
showing positive
or
negative tone, because they are the only Tweets that used the above
symbols.
On the
day I used Twitter Search to run a sentiment search on #unfollowdiddy,
it was the most popular topic of the day, with 2629 tweets (according
to
#hashtags.org.) But my search brought up only a single tweet,
which Twitter Search mistakenly rated as positive:
Knot2serious:
@TheTZA Glad to see you doing the #unfollowdiddy thing too! :D He
really has a crappy ego & no talent. He's all hype.
So, how are you? :) :P
The
eye of the beholder: The above tweet exemplifies a major
problem with sentiment search: Sentiment is many times a matter
of perspective.
Most
sentiment analyses grade an item's sentiment based on the overall tone
of the message and not necessarily the sentiment towards the subject of
the message. For
example, the above tweet has three positive sentiment symbols,
yet the words
make it clear that the tweet is actually extremely
negative towards P. Diddy. To be fair to Twitter Search, all the
tools reviewed here suffer from the same difficulty.
(#unfollowdiddy
was a controversial effort to encourage people to stop following
P. Diddy on Twitter. Each
post on the subject usually included a reason why people should
unfollow
him. Taken from P. Diddy's perspective, most of those posts would
be considered negative. Taken from the perspective of the person
who started #unfollowdiddy, most of the posts attacking P. Diddy
would be considered positive.)
Conclusion: Pretty
much useless. If
you are trying to gauge the overall tone of a topic or Twitter user,
then Twitter Search is not the tool to use. Symbols should be part
of a sentiment search, but they shouldn't be the only part. Twitter
Search is sentiment search in its infancy,
at best: It's a fun Twitter
toy people
can
use if they want to see how many happy or unhappy faces were
used in a tweet.
Social
Mention
Social
Mention, a social media search platform, assigns a tone/sentiment
to every tweet: Positive, neutral, or negative,
distinguished on their charts by color (green,
gray, red). They also provide a sentiment ratio designed to give
you an overall feel for the tone of a given topic. Social
Mention
offers an exhaustive breakdown of information by top keywords,
top users,
top hashtags, and top sources beyond Twitter. Their breakdown column
lets you sort by sentiment as well, allowing you to see all the positive,
neutral, or negative tweets grouped together.
According
to Social
Mention's creator, Jon Cianciullo, they, "use
textual analysis, symbols, emoticons, and a few other things," to
measure tweet sentiment. "We developed a method to achieve the highest
level of accuracy that was reasonable to implement. We based it on
some great open source projects and designed it specifically for
the type of media we process. We leverage word, symbol, and phrase
analysis to yield a ratio which is then used to grade the overall
sentiment."
I tweeted
back and forth a little bit with him, and he told me candidly that
sentiment
analysis is extremely difficult. Their research has found Social
Mention's
sentiment
analysis to be roughly 60% - 80% accurate. He also said
that they are developing a new Social Mention product which will
provide more analysis, saved searches,
and
reporting.
Based
on
a #unfollowdiddy
analysis using Social Mention, those numbers seem to be accurate.
Social Mention's Sentiment
Ratio was listed as 2:1, twice as much positive as negative. Of
course it's all a matter of perspective as I noted above, (for Twitter
Search), and as demonstrated by the following examples:
- Mistakenly
listed as having positive sentiment:
- 1. @jamalahmad
lol yeah the #unfollowdiddy is getting pretty old and quite mean,
why cant they just not follow him like us?
- 2. #unfollowdiddy
cuz it's his fault that the Palm Pre ain't out in stores yet, they
had to put a Let's Go button on it, BOOO, lol...
- Mistakenly
listed as having negative sentiment:
- 1. got
lots done...time to relax but I won't #UnFollowDiddy!! Go diddy go!
- 2. #unfollowdiddy
or #followdiddy.. oh shit damn he so gooood lol
Conclusion: Moderately
useful. If
60-80% accuracy is close enough, then Social Mention is a tool you
can use. It's the best overall among the five reviewed here.
Twendz
(beta)
The Twendz
site, a project of Waggener
Edstrom Worldwide, is an in-the-works-project entirely devoted
to measuring sentiment in Twitter conversations. It uses a combination
of keywords
and symbols
to
compare and cross reference against a dictionary to make
an educated guess on the sentiment of the posts.
It measures tweets as positive, neutral or negative.
Beyond that it breaks down sentiment by topic and then by reoccurring
words found in the tweets. Twendz lets you highlight all tweets by
individual tone type as well.
Twendz
is really geared toward current topics. When a search term is
entered, it pulls the 70 most recent tweets with that term, and then
updates the results as newer tweets
on that subject come in. There is currently no way to look
at tweets prior
to the first 70 they pull up. My impression is that Twendz is somewhat
less accurate than Social Mention.
Conclusion: May
be useful,
depending on your needs. If you are only interested in the most recent
70 tweets, great. If you want to do any history at all, this one
can't do it.
Twitter
Sentiment
Twitter
Sentiment, also entirely devoted to measuring sentiment in Twitter
conversations, is, as it says, "strictly a school project." Like
many of the other sites it ranks sentiment as positive, neutral,
or negative (distinguished
by color – green, white, red). It's probably the simplest
looking of the five sites reviewed here.
It offers
a feature that allows you to change the sentiment results if you
think they
aren't
correct and submit your feedback. That doesn't mean that your
edit becomes permanent, but I like the idea.
The bad
news -- really bad news -- is that no matter what topic you search,
Twitter
Sentiment only pulls
up one
page of results: If you click the "next page" button,
it starts your search over. So your results are limited
to about 17 or so tweets. With such a small sample,
it's hard to determine how accurate Twitter Sentiment is. The
good
news is that the site's "submit
feedback" button
works. So you can tell them the "next page" button
doesn't work. So maybe they'll fix it.
Conclusion: Very
limited usefulness. For a school project, it probably
gets a A. But in the real world, it's only useful if you are only interested
in the most recent 15-20 tweets. I'd use Twendz before I'd use this
one.
Twitrratr
Twitrratr
is another site devoted to tracking sentiment on Twitter. It
cross-references your search term against a dictionary of positive
and negative keywords. It offers
a clean, polished dashboard
that
shows the
total number
of tweets containing your search term and how many were positive,
neutral, or negative. The individual tweets are then shown in corresponding
columns, with the category-triggering words highlighted.
At the
bottom of the page are links showing exactly which words and symbols
count as positive and which
words and symbols
count as negative. Twitrratr shows 174 positive words, abbreviations,
and symbols, and 185 negative words, abbreviations, and symbols.
Certainly not an exhaustive search and a lot of room for contextual
error,
but at least you know what you're dealing with.
My impression is that there were not enough tweets categorized negative,
and that Twitrratr is less accurate than Twendz.
At the
top of the page they show the number of all the posts, but
I couldn't find any way to actually see any of the other posts
beyond the results that appear on the first page. Under
their About link they make it quite clear that this site is a Startup
Weekend project, and a work in progress. At least
they are aware of their
limitations and freely admit it.
Conclusion: Only
moderately
useful. Smooth layout, and the total numbers are nice,
but still limited by lack of history. I'd use Social Mention rather
than
Twitrratr.
A bit
limited
in
concept right now, but future potential to watch for.
And
finally...
The current
efforts to track sentiment through a machine are a great beginning
and, even for the tools I ranked poorly, what they've accomplished
thus far is quite impressive. The
best of the group is Social Mention, but it's certainly
not accurate enough
for many purposes.
The big
picture here is that, although there is demand for free
automated
sentiment
search,
the
technology to pull it off isn't quite there, at least not yet. What
it comes down to is: What percentage of accuracy are we willing to
live with? 
Don't
miss Chris Near's other
article on measuring Twitter, also in this issue of The
Measurement Standard: "Which
Twitter Profile Analysis Tool Rules the Nest?"
Chris
Near is Director of Research for KDPaine & Partners.
Chris recently graduated with his master's in communications and
currently devotes most of his time to measuring PR and developing
social media methodologies. That is, of course, when he's not at
home tending to his lovely wife, Valerie, or chasing around his tireless
two year-old son, Brendan.
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