[SOUND] This lecture is a continued
discussion of
Latent Aspect Rating Analysis.
Earlier, we talked about how to solve
the problem of LARA in two stages.
But we first do segmentation
of different aspects.
And then we use a latent regression
model to learn the aspect ratings and
then later the weight.
Now it's also possible to develop
a unified generative model for
solving this problem, and
that is we not only model the generational
over-rating based on text.
We also model the generation of text,
and so
a natural solution would
be to use topic model.
So given the entity,
we can assume there are aspects that
are described by word distributions.
Topics.
And then we an use a topic model to model
the generation of the reviewed text.
I will assume words in the review text
are drawn from these distributions.
In the same way as we assumed for
generating model like PRSA.
And then we can then plug in
the latent regression model to
use the text to further
predict the overrating.
And that means when we first
predict the aspect rating and
then combine them with aspect weights
to predict the overall rating.
So this would give us
a unified generated model,
where we model both the generation of text
and the overall ready condition on text.
So we don't have time to discuss
this model in detail as in
many other cases in this part of the cause
where we discuss the cutting edge topics,
but there's a reference site here
where you can find more details.
So now I'm going to show you some
simple results that you can get
by using these kind of generated models.
First, it's about rating decomposition.
So here, what you see
are the decomposed ratings for
three hotels that have
the same overall rating.
So if you just look at the overall rating,
you can't really tell much
difference between these hotels.
But by decomposing these
ratings into aspect ratings
we can see some hotels have higher
ratings for some dimensions,
like value, but others might score better
in other dimensions, like location.
And so this can give you detailed
opinions at the aspect level.
Now here, the ground-truth is
shown in the parenthesis, so
it also allows you to see whether
the prediction is accurate.
It's not always accurate but It's mostly
still reflecting some of the trends.
The second result you compare
different reviewers on the same hotel.
So the table shows the decomposed ratings
for two reviewers about same hotel.
Again their high level
overall ratings are the same.
So if you just look at the overall
ratings, you don't really get that much
information about the difference
between the two reviewers.
But after you decompose the ratings,
you can see clearly that they have
high scores on different dimensions.
So this shows that model can review
differences in opinions of different
reviewers and such a detailed
understanding can help us understand
better about reviewers and also better
about their feedback on the hotel.
This is something very interesting,
because this is in some
sense some byproduct.
In our problem formulation,
we did not really have to do this.
But the design of the generating
model has this component.
And these are sentimental weights for
words in different aspects.
And you can see the highly weighted words
versus the negatively loaded weighted
words here for
each of the four dimensions.
Value, rooms, location, and cleanliness.
The top words clearly make sense, and
the bottom words also make sense.
So this shows that with this approach,
we can also learn sentiment
information directly from the data.
Now, this kind of lexicon is very useful
because in general, a word like long,
let's say, may have different sentiment
polarities for different context.
So if I say the battery life of this
laptop is long, then that's positive.
But if I say the rebooting time for
the laptop is long, that's bad, right?
So even for
reviews about the same product, laptop,
the word long is ambiguous, it could
mean positive or it could mean negative.
But this kind of lexicon, that we can
learn by using this kind of generated
models, can show whether a word is
positive for a particular aspect.
So this is clearly very useful, and in
fact such a lexicon can be directly used
to tag other reviews about hotels or
tag comments about hotels in
social media like Tweets.
And what's also interesting is that since
this is almost completely unsupervised,
well assuming the reviews whose
overall rating are available And
then this can allow us to learn form
potentially larger amount of data on
the internet to reach sentiment lexicon.
And here are some results to
validate the preference words.
Remember the model can infer wether
a reviewer cares more about service or
the price.
Now how do we know whether
the inferred weights are correct?
And this poses a very difficult
challenge for evaluation.
Now here we show some
interesting way of evaluating.
What you see here are the prices
of hotels in different cities, and
these are the prices of hotels that are
favored by different groups of reviewers.
The top ten are the reviewers
was the highest
inferred value to other aspect ratio.
So for example value versus location,
value versus room, etcetera.
Now the top ten of the reviewers that
have the highest ratios by this measure.
And that means these reviewers
tend to put a lot of
weight on value as compared
with other dimensions.
So that means they really
emphasize on value.
The bottom ten on the other
hand of the reviewers.
The lowest ratio, what does that mean?
Well it means these reviewers have
put higher weights on other aspects
than value.
So those are people that cared about
another dimension and they didn't care so
much the value in some sense, at least
as compared with the top ten group.
Now these ratios are computer based on
the inferred weights from the model.
So now you can see the average prices
of hotels favored by top ten reviewers
are indeed much cheaper than those
that are favored by the bottom ten.
And this provides some indirect way
of validating the inferred weights.
It just means the weights are not random.
They are actually meaningful here.
In comparison,
the average price in these three cities,
you can actually see the top ten
tend to have below average in price,
whereas the bottom half, where they care
a lot about other things like a service or
room condition tend to have hotels
that have higher prices than average.
So with these results we can build
a lot of interesting applications.
For example, a direct application would be
to generate the rated aspect, the summary,
and because of the decomposition we
have now generated the summaries for
each aspect.
The positive sentences the negative
sentences about each aspect.
It's more informative than original review
that just has an overall rating and
review text.
Here are some other results
about the aspects that's covered
from reviews with no ratings.
These are mp3 reviews,
and these results show that the model
can discover some interesting aspects.
Commented on low overall ratings versus
those higher overall per ratings.
And they care more about
the different aspects.
Or they comment more on
the different aspects.
So that can help us discover for
example, consumers'
trend in appreciating different
features of products.
For example, one might have discovered
the trend that people tend to
like larger screens of cell phones or
light weight of laptop, etcetera.
Such knowledge can be useful for
manufacturers to design their
next generation of products.
Here are some interesting results
on analyzing users rating behavior.
So what you see is average weights
along different dimensions by
different groups of reviewers.
And on the left side you see the weights
of viewers that like the expensive hotels.
They gave the expensive hotels 5 Stars,
and
you can see their average rates
tend to be more for some service.
And that suggests that people like
expensive hotels because of good service,
and that's not surprising.
That's also another way to
validate it by inferred weights.
If you look at the right side where,
look at the column of 5 Stars.
These are the reviewers that
like the cheaper hotels, and
they gave cheaper hotels five stars.
As we expected and
they put more weight on value,
and that's why they like
the cheaper hotels.
But if you look at the, when they didn't
like expensive hotels, or cheaper hotels,
then you'll see that they tended to
have more weights on the condition of
the room cleanness.
So this shows that by using this model,
we can infer some
information that's very hard to obtain
even if you read all the reviews.
Even if you read all the reviews it's
very hard to infer such preferences or
such emphasis.
So this is a case where text mining
algorithms can go beyond what
humans can do, to review
interesting patterns in the data.
And this of course can be very useful.
You can compare different hotels,
compare the opinions from different
consumer groups, in different locations.
And of course, the model is general.
It can be applied to any
reviews with overall ratings.
So this is a very useful
technique that can
support a lot of text mining applications.
Finally the results of applying this
model for personalized ranking or
recommendation of entities.
So because we can infer the reviewers
weights on different dimensions,
we can allow a user to actually
say what do you care about.
So for example, I have a query
here that shows 90% of the weight
should be on value and 10% on others.
So that just means I don't
care about other aspect.
I just care about getting a cheaper hotel.
My emphasis is on the value dimension.
Now what we can do with such query
is we can use reviewers that we
believe have a similar preference
to recommend a hotels for you.
How can we know that?
Well, we can infer the weights of
those reviewers on different aspects.
We can find the reviewers whose
weights are more precise,
of course inferred rates
are similar to yours.
And then use those reviewers to
recommend hotels for you and
this is what we call personalized or
rather query specific recommendations.
Now the non-personalized
recommendations now shown on the top,
and you can see the top results generally
have much higher price, than the lower
group and that's because when the
reviewer's cared more about the value as
dictated by this query they tended
to really favor low price hotels.
So this is yet
another application of this technique.
It shows that by doing text mining
we can understand the users better.
And once we can handle users better
we can solve these users better.
So to summarize our discussion
of opinion mining in general,
this is a very important topic and
with a lot of applications.
And as a text sentiment
analysis can be readily done by
using just text categorization.
But standard technique
tends to not be enough.
And so we need to have enriched
feature implementation.
And we also need to consider
the order of those categories.
And we'll talk about ordinal
regression for some of these problem.
We have also assume that
the generating models are powerful for
mining latent user preferences.
This in particular in the generative
model for mining latent regression.
And we embed some interesting
preference information and
send the weights of words in the model
as a result we can learn most
useful information when
fitting the model to the data.
Now most approaches have been proposed and
evaluated.
For product reviews, and that was because
in such a context, the opinion holder and
the opinion target are clear.
And they are easy to analyze.
And there, of course,
also have a lot of practical applications.
But opinion mining from news and
social media is also important, but that's
more difficult than analyzing review data,
mainly because the opinion holders and
opinion targets are all interested.
So that calls for
natural management processing
techniques to uncover them accurately.
Here are some suggested readings.
The first two are small books that
are of some use of this topic,
where you can find a lot of discussion
about other variations of the problem and
techniques proposed for
solving the problem.
The next two papers about
generating models for
rating the aspect rating analysis.
The first one is about solving
the problem using two stages, and
the second one is about a unified model
where the topic model is integrated
with the regression model to solve
the problem using a unified model.
[MUSIC]

