[MUSIC] This lecture is about the Latent Aspect Rating Analysis for Opinion Mining and Sentiment Analysis. In this lecture, we're going to continue discussing Opinion Mining and Sentiment Analysis. In particular, we're going to introduce Latent Aspect Rating Analysis which allows us to perform detailed analysis of reviews with overall ratings. So, first is motivation. Here are two reviews that you often see in the net about the hotel. And you see some overall ratings. In this case, both reviewers have given five stars. And, of course, there are also reviews that are in text. Now, if you just look at these reviews, it's not very clear whether the hotel is good for its location or for its service. It's also unclear why a reviewer liked this hotel. What we want to do is to decompose this overall rating into ratings on different aspects such as value, rooms, location, and service. So, if we can decompose the overall ratings, the ratings on these different aspects, then, we can obtain a more detailed understanding of the reviewer's opinionsabout the hotel. And this would also allow us to rank hotels along different dimensions such as value or rooms. But, in general, such detailed understanding will reveal more information about the user's preferences, reviewer's preferences. And also, we can understand better how the reviewers view this hotel from different perspectives. Now, not only do we want to infer these aspect ratings, we also want to infer the aspect weights. So, some reviewers may care more about values as opposed to the service. And that would be a case. like what's shown on the left for the weight distribution, where you can see a lot of weight is places on value. But others care more for service. And therefore, they might place more weight on service than value. The reason why this is also important is because, do you think about a five star on value, it might still be very expensive if the reviewer cares a lot about service, right? For this kind of service, this price is good, so the reviewer might give it a five star. But if a reviewer really cares about the value of the hotel, then the five star, most likely, would mean really cheap prices. So, in order to interpret the ratings on different aspects accurately, we also need to know these aspect weights. When they're combined together, we can have a more detailed understanding of the opinion. So the task here is to get these reviews and their overall ratings as input, and then, generate both the aspect ratings, the compose aspect ratings, and the aspect rates as output. And this is a problem called Latent Aspect Rating Analysis. So the task, in general, is given a set of review articles about the topic with overall ratings, and we hope to generate three things. One is the major aspects commented on in the reviews. Second is ratings on each aspect, such as value and room service. And third is the relative weights placed on different aspects by the reviewers. And this task has a lot of applications, and if you can do this, and it will enable a lot of applications. I just listed some here. And later, I will show you some results. And, for example, we can do opinion based entity ranking. We can generate an aspect-level opinion summary. We can also analyze reviewers preferences, compare them or compare their preferences on different hotels. And we can do personalized recommendations of products. So, of course, the question is how can we solve this problem? Now, as in other cases of these advanced topics, we wonâ€™t have time to really cover the technique in detail. But Iâ€™m going to give you a brisk, basic introduction to the technique development for this problem. So, first step, weâ€™re going to talk about how to solve the problem in two stages. Later, weâ€™re going to also mention that we can do this in the unified model. Now, take this review with the overall rating as input. What we want to do is, first, we're going to segment the aspects. So we're going to pick out what words are talking about location, and what words are talking about room condition, etc. So with this, we would be able to obtain aspect segments. In particular, we're going to obtain the counts of all the words in each segment, and this is denoted by C sub I of W and D. Now this can be done by using seed words like location and room or price to retrieve the [INAUDIBLE] in the segments. And then, from those segments, we can further mine correlated words with these seed words and that would allow us to segmented the text into segments, discussing different aspects. But, of course, later, as we will see, we can also use [INAUDIBLE] models to do the segmentation. But anyway, that's the first stage, where the obtain the council of words in each segment. In the second stage, which is called Latent Rating Regression, we're going to use these words and their frequencies in different aspects to predict the overall rate. And this predicting happens in two stages. In the first stage, we're going to use the [INAUDIBLE] and the weights of these words in each aspect to predict the aspect rating. So, for example, if in your discussion of location, you see a word like, amazing, mentioned many times, and it has a high weight. For example, here, 3.9. Then, it will increase the Aspect Rating for location. But, another word like, far, which is an acted weight, if it's mentioned many times, and it will decrease the rating. So the aspect ratings, assume that it will be a weighted combination of these word frequencies where the weights are the sentiment weights of the words. Of course, these sentimental weights might be different for different aspects. So we have, for each aspect, a set of term sentiment weights as shown here. And that's in order by beta sub I and W. In the second stage or second step, we're going to assume that the overall rating is simply a weighted combination of these aspect ratings. So we're going to assume we have aspect weights to the [INAUDIBLE] sub i of d, and this will be used to take a weighted average of the aspect ratings, which are denoted by r sub i of d. And we're going to assume the overall rating is simply a weighted average of these aspect ratings. So this set up allows us to predict the overall rating based on the observable frequencies. So on the left side, you will see all these observed information, the r sub d and the count. But on the right side, you see all the information in that range is actually latent. So, we hope to discover that. Now, this is a typical case of a generating model where would embed the interesting variables in the generated model. And then, we're going to set up a generation probability for the overall rating given the observed words. And then, of course, we can adjust these parameter values including betas Rs and alpha Is in order to maximize the probability of the data. In this case, the conditional probability of the observed rating given the document. So we have seen such cases before in, for example, PISA, where we predict a text data. But here, we're predicting the rating, and the parameters, of course, are very different. But we can see, if we can uncover these parameters, it would be nice, because r sub i of d is precise as the ratings that we want to get. And these are the composer ratings on different aspects. [INAUDIBLE] sub I D is precisely the aspect weights that we hope to get as a byproduct, that we also get the beta factor, and these are the [INAUDIBLE] factor, the sentiment weights of words. So more formally, the data we are modeling here is a set of review documents with overall ratings. And each review document denote by a d, and the overall ratings denote by r sub d. And d pre-segments turn into k aspect segments. And we're going to use ci(w,d) to denote the count of word w in aspect segment i. Of course, it's zero if the word doesn't occur in the segment. Now, the model is going to predict the rating based on d. So, we're interested in the provisional problem of r sub-d given d. And this model is set up as follows. So r sub-d is assumed the two follow a normal distribution doesn't mean that denotes actually await the average of the aspect of ratings r Sub I of d as shown here. This normal distribution is a variance of data squared. Now, of course, this is just our assumption. The actual rating is not necessarily anything thing this way. But as always, when we make this assumption, we have a formal way to model the problem and that allows us to compute the interest in quantities. In this case, the aspect ratings and the aspect weights. Now, the aspect rating as you see on the [INAUDIBLE] is assuming that will be a weight of sum of these weights. Where the weight is just the [INAUDIBLE] of the weight. So as I said, the overall rating is assumed to be a weighted average of aspect ratings. Now, these other values, r for sub I of D, or denoted together by other vector that depends on D is that the token of specific weights. And weâ€™re going to assume that this vector itself is drawn from another Multivariate Gaussian distribution, with mean denoted by a Mu factor, and covariance metrics sigma here. Now, so this means, when we generate our overall rating, we're going to first draw a set of other values from this Multivariate Gaussian Prior distribution. And once we get these other values, we're going to use then the weighted average of aspect ratings as the mean here to use the normal distribution to generate the overall rating. Now, the aspect rating, as I just said, is the sum of the sentiment weights of words in aspect, note that here the sentiment weights are specific to aspect. So, beta is indexed by i, and that's for aspect. And that gives us a way to model different segment of a word. This is neither because the same word might have positive sentiment for another aspect. It's also used for see what parameters we have here beta sub i and w gives us the aspect-specific sentiment of w. So, obviously, that's one of the important parameters. But, in general, we can see we have these parameters, beta values, the delta, and the Mu, and sigma. So, next, the question is, how can we estimate these parameters and, so we collectively denote all the parameters by lambda here. Now, we can, as usual, use the maximum likelihood estimate, and this will give us the settings of these parameters, that with a maximized observed ratings condition of their respective reviews. And of, course, this would then give us all the useful variables that we are interested in computing. So, more specifically, we can now, once we estimate the parameters, we can easily compute the aspect rating, for aspect the i or sub i of d. And that's simply to take all of the words that occurred in the segment, i, and then take their counts and then multiply that by the center of the weight of each word and take a sum. So, of course, this time would be zero for words that are not occurring in and that's why were going to take the sum of all the words in the vocabulary. Now what about the s factor weights? Alpha sub i of d, well, it's not part of our parameter. Right? So we have to use that to compute it. And in this case, we can use the Maximum a Posteriori to compute this alpha value. Basically, we're going to maximize the product of the prior of alpha according to our assumed Multivariate Gaussian Distribution and the likelihood. In this case, the likelihood rate is the probability of generating this observed overall rating given this particular alpha value and some other parameters, as you see here. So for more details about this model, you can read this paper cited here. [MUSIC]