[Build Backlinks Online] How I Wish Amazon Reviews Worked

Build Backlinks Online has posted a new item, 'How I Wish Amazon Reviews Worked'


Posted by Dr. Pete

This is not a post about SEO. It is, however, a post about the future of
search. This surprised even me when I started writing this piece, it really was
just an idea about building a better review. I realized, though, that finding
relevant reviews is a useful microcosm of the broader challenge search engines
face. Specifically, I want to talk about three Ss Social, Sentiment, and
Semantics, and how each of these pieces fit the search puzzle. Along the way, I
might just try to build a better mousetrap.

The Core Problem


Product reviews are great, but on a site as big and popular as Amazon.com,
filtering reviews isnt much easier than filtering Google search results. Heres
the review section for the Kindle Fire:



Thats right 10,859 reviews to sort through. Even if I just decide to look at
the 5 stars and 1 stars, thats still 7,208 reviews. If I could click and skim
each one of those 7,208 in about 5 seconds, Ive got roughly 10 hours of
enjoyment ahead of me (if I dont eat or take bathroom breaks). So, how can we
make this system better?

(1) The Social Graph


These days our first answer is usually: SOCIAL! Social is sexy, and it will
solve all our problems with its sexy sexiness. The problem is that we tend to
oversimplify. Heres how we think about Search + Social, in our perfect world:



Unfortunately, its not quite so magical. There are two big problems, whether
were talking about product reviews or organic search results. The first problem
is a delicate one. Some of the people that you associate with are how shall I
put it stupid.

Ok, maybe stupid is a bit harsh, but just because youre connected to someone
doesnt mean you have a lot in common or share the same tastes. So, we really
want to weed out some of the intersection, like Crazy Cousin Larry



Its surprisingly hard to figure out who we actually sit at the Crazy-Larry
table. Computationally, this is a huge challenge. Theres a bigger problem,
though. In most cases, especially once we start weeding people out, the picture
actually looks more like this:



Even with relatively large social circles, the actual overlap of your network
and any given search result or product is often so small as to be useless. We
can extend our circles to 2nd- and 3rd-degree relationships, but then relevance
quickly suffers.

To be fair to Amazon, theyve found one solution they elicit user feedback of
the reviews themselves as a proxy social signal:



This approach certainly helps, but it mostly weeds out the lowest-quality
offerings. Reviews of reviews help control quality, but they don't do much to
help us find the most relevant information.

(2) Sentiment Analysis


Reviews are a simple form of sentiment analysis they help us determine if
people view a product positively or negatively. More advanced sentiment analysis
uses natural-language processing (NLP) to try to extract the emotional tone of
the text.

You may be wondering why we need more advanced sentiment analysis when someone
has already told us how they feel on a 1-5 scale. Welcome to what I call The
Cupholder Problem, something Ive experienced frequently as a parent trying to
buy high-end products on Amazon. Consider this fictional review which is
all-too-based in reality:



Im exaggerating, of course, but the core problem is that reviews are entirely
subjective, and sometimes just one feature or problem can ruin a product for
someone. Once that text is reduced to a single data point (one star), though,
the rest of the information in the content is lost.

Sentiment analysis probably wouldnt have a dramatic impact on Amazon reviews,
but its a hot topic in search in general because it can help extract emotional
data thats sometimes lost in a summary (whether its a snippet or a star rating).
It might be nice to see Amazon institute some kind of sentiment correction
process, warning people if the tone of their review doesnt seem to match the
star rating.

(3) Semantic Search


This is where things get interesting (and I promise Ill get back to sentiment
so that the previous section has a point). The phrase semantic search has been
abused, unfortunately, but the core idea is to get at the meaning and conceptual
frameworks behind information. Google Knowledge Graph is probably the most
visible, recent attempt to build a system that extracts concepts and even
answers, instead of just a list of relevant documents.

How does this help our review problem? Lets look at the Thirsty example again.
Its not a dishonest review or even useless the problem is that I fundamentally
dont care about cupholders. There are certain features that matter a lot to me
(safety, weight, durability), others that Im only marginally sensitive to
(price, color), and some that I dont care about at all (beverage dispensing
capability).

So, what if we could use a relatively simple form of semantic analysis to
extract the salient features from reviews for any given product? We might end up
with something like this:



Pardon the uninspired UI, but even the addition of a few relevant features
could help customers drill down to what really matters to them, and this could
be done with relatively simple semantic analysis. This basic idea also
illustrates some of the direction I think search is heading. Semantic search
isnt just about retrieving concepts; its also about understanding the context of
our questions.

Heres an interesting example from Google Australia (Google.com.au). Search for
Broncos colors and youll get this answer widget (hat tip to Brian Whalley for
spotting these):



Its hardly a thing of beauty, but it gets the job done and probably answers the
query for 80-90% of searches. This alone is an example of search returning
concepts and not just documents, but it gets even more interesting. Now search
for Broncos colours, using the British spelling (still in Google.com.au). You
should get this answer:



The combination of Google.com.au and the Queens English now has Google assuming
that you meant Australias own Brisbane Broncos. This is just one tiny taste of
the beginning of search using concepts to both deliver answers and better
understand the questions.

(4) Semantics + Sentiment


Lets bring this back around to my original idea. What if we could combine
semantic analysis (feature extraction) and sentiment in Amazon reviews? We could
easily envision a system like this:



Ive made one small addition a positive or negative (+/-) sentiment choice next
to each feature. Maybe I only want to see products where people spoke highly of
the value, or rule out the ones where they bashed the safety. Even a few simple
combinations could completely change the way you digest this information.

The Tip of the Penguin


This isnt the tip of the iceberg its the flea on the wart on the end of the
penguins nose on the tip of the iceberg. We still think of Knowledge Graph and
other semantic search efforts as little more than toys, but theyre building a
framework that will revolutionize the way we extract information from the
internet over the next five years. I hope this thought exercise has given you a
glimpse into how powerful even a few sources of information can be, and why
theyre more powerful together than alone. Social doesnt hold all of the answers,
but it is one more essential piece of a richer puzzle.

Id also like to thank you for humoring my Amazon reviews insanity. To be fair
to Amazon, theyve invested a lot into building better systems, and Im sure they
have fascinating ideas in the pipe. If theyd like to use any of these ideas, Im
happy to sell them for the very reasonable price of ONE MILL-I-ON DOLLARS.
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten
hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think
of it as your exclusive digest of stuff you don't have time to hunt down but
want to read!






You may view the latest post at
http://feedproxy.google.com/~r/seomoz/~3/ajSR3nUzRMY/how-i-wish-amazon-reviews-worked

You received this e-mail because you asked to be notified when new updates are
posted.
Best regards,
Build Backlinks Online
peter.clarke@designed-for-success.com