Discussion Paper No. 74
Interpretable Perceived Topics
in Online Customer Reviews
for Product Satisfaction and Expectation
Aijing Xing
Nobuhiko TeruiFebruary , 2018

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANInterpretable Perceived Topics in Online Customer Reviews
for Product Satisfaction and Expectation

Aijing Xing and Nobuhiko Terui 1

Graduate School of Economics and Management
Tohoku University

February, 2018

Tohoku University, Graduate School of Economics and Management, Kawauchi Aoba-ku, Sendai,
980-8576, Japan; terui@econ.tohoku.ac.jp
1
Terui acknowledges a grant by JSPS KAKENHI Grant Number (A)17H01001.
1Interpretable Perceived Topics in Online Customer Reviews
for Product Satisfaction and Expectation

Abstract
Online customer reviews contain useful and important information, in particular, for
product management—because customers tend to praise or criticize certain features or
attributes of goods in their reviews.

We propose a model that extracts the perceived topics

from textual reviews by natural language processing under the restrictions of theirinterpretability and predictability of product satisfaction as current product evaluation and
expectation as future possible demand by supervised learning.

The empirical analysis on

user-generated content of food reviews shows that our proposed model performs better thanalternative models, and it suggests product managers the necessity of improving somespecific attributes and focus their advertising on these attributes as fulfilling customer needs.

Keywords and Phrases: Food satisfaction, Online customer reviews, Labeled Topic model,
Supervised learning, User-generated content, Text data2

1 Introduction
With the rise of electronic commerce, online retailers such as Amazon, Walmart, andTaobao have experienced growth in their number of users.

Most online retailers havecustomer feedback systems to receive information, including satisfaction scores (also called
product ratings) and textual reviews.
Various methods to utilize online customer review data have been proposed in theliterature.

Some evidence suggests that consumer reviews are important to get the approval

from readers and further facilitate purchase decisions and product sales.

As

customer-created information, online customer reviews are likely to be more credible thanseller-created information (Wilson and Sherrell, 1993). Online customer reviews posted inforums are relevant to consumers because the opinions and reviews are contributed by fellow
consumers, who are perceived to have no vested interest in the product and no intentions tomanipulate the reader (Bickart and Schindler, 2001).

The number of customer reviews

affects the profit of book products (Zhao et al., 2013).

Chen and Xie (2008) suggest thatonline reviews can help novice customers identify products that best match their preferences,and they conclude that in the absence of review information, novice consumers may be lesslikely to buy a product if only seller-created product attribute information is available.
Obviously customers prefer to read the customer reviews before purchase decision, because
these user-generated information is perceived to be trustworthy and match their preference.
The satisfaction scores were used as indicators of word-of-mouth (WOM) to forecast
product sales and revenue.

Chevalier and Mayzlin (2006) find that the star rating of online

book products has a significant impact on book sales.
3

Liu (2006) shows that movie ratings

on the Yahoo! Movies website have a significant effect on box office revenue.

Chintagunta

et al. (2010) find that the valence of online WOM (mean user rating) has a significant andpositive impact on box office earnings.
Performing text analysis of online customer reviews is also useful for improving aseller’s marketing strategy and customer management.

Company managers can minetextual information about product features from the “voice of the consumer” and hence better
meet customer needs when proposing new products (Fay and Xie, 2008, Griffin and Hauser,
1993, Xie and Gestner, 2007, Yohan and Alice, 2011).

Lee and Bradlow (2011) and Netzer

et al. (2012) exploit text-mined semi-structured product reviews to understand market
structures based on the product attributes mentioned in the reviews.

Decker and Trusov(2010) estimate consumer preferences for product attributes by text mining pro and con
product reviews.
Büschken and Allenby (2016) recognize that the informative aspects of text data arereadily observed and can directly serve as explanatory variables to identify customer behavior(e.g., satisfied versus unsatisfied experiences, helpful and unhelpful levels), and they assume
that words within a sentence pertain to the same topic. Their sentence-based text analysis
improves the predictive fit of customer satisfaction rating.
Except for the content of customer review, exploring the influences of reviews toward
purchase intention is also important. Maheswaran and Levy (1990) find that positively andlittle detailed framed messages may be persuasive, and negatively and detailed framed
messages may also be persuasive.

Herr et al (1991) conclude that vivid WOM about

product attribute information has a greater impact on customer choices.
4

We assume that

readers of these reviews expect to search for valuable information from the experienced
customers, such as risk of the product or favorable recommendation, and these information
will be important reason for purchase decision of readers.
Following empirical research above, this study aims at information extraction from
review data and addresses two questions about customer experience and expectation:“What product features are mentioned in the online customer reviews posted bysatisfied and dissatisfied customers?”“Whether readers expect to find helpful information about product features incustomer reviews.”
To answer these questions, we extend topic model for extracting perceived topics containing
product features and predict satisfaction rating and helpfulness of product expectation.
Topic models such as Latent Dirichlet Allocation (LDA) (Blei et al., 2003) assumes
each customer review consists of several topics with varying probabilities.
problem of LDA model is the lack of interpretability.

The main

Promiscuous words sometimes are

clustered in a common topic, which makes the topics hard to interpret.

To improve

interpretability, several modifications of LDA have been proposed in terms of incorporate
supervision.

Supervised LDA (Blei and McAuliffe, 2007) accommodates movie reviews(text data) and movie scoring (ordered categorical data), and a regression procedure is used
for topic extraction and movie scoring forecasting.

In labeled LDA (Ramage et al., 2009),

documents are labeled with perceived tags artificially, and the topics can be easilyinterpreted.
Another focus of this paper is assuming the perceived topics according to prior5

information-product features mentioned in customer reviews.

By reading the subset ofcustomer reviews and observing the descending sort of word frequencies from the whole
vocabulary, we employ specific perceived topics about product features of the customer
reviews for the labeling.
This paper proposes the supervised–labeled LDA model by combining supervised
LDA and labeled LDA to extract interpretable perceived latent topics about product featuresfrom online customer reviews and predict customer satisfaction and product expectation.
Then the number of word assigned to respective topic taking logarithm is used to forecast
customer’s satisfaction ratings and helpfulness of product expectation.
In section 2, we first illustrate the layout form of customer reviews, which contains
information about textual reviews and numerical variables, and we investigate the features ofresearch object(one brand of potato chips) through reading the subset of customer reviews,
observing the descending sort of word frequency list of the whole vocabulary, and referred to
the literature about food features, thereby we presuppose the perceived topics. To improve
interpretability and make the perceived topics match with product features, we propose thesupervised–labeled LDA model in the form of multivariate linear regression and discuss theestimation procedure in section 3.

The section 4 reports the empirical analysis.

We discuss

managerial implications in section 5 and conclude in section 6 finally.

2. Online Customer Review
2.1. Layout Form of Online Customer Reviews
Most online retailers have a similar layout form of online customer reviews, including6

textual content and product rating. For instance, the Amazon review in Figure 1 includes
three parts: (i)Satisfaction Score, (ii)Textual Review, and (iii)Helpfulness Numerator.
Specifically, after buying and accepting goods, customers are allowed post their feelings andexperiences by writing a textual review.

At the same time, these customers may grade

products with “stars” ranging from 1 to 5. The reader of this online review evaluate whether
this review helpful for his/her product expectation and purchase decision by clicking “Yes” orunhelpful by clicking “No”.
Figure 1. Layout form of Amazon food review

2.2. Textual Reviews and Perceived Topics
The “Amazon Fine Food Reviews” data is available at the website of “Kaggle”.
This site is a platform for predictive modeling and analytics competitions, and the datasets
are freely uploaded by companies and users.) The data set consists of about 500,000 foodreviews by Amazon users up till October, 2012 on products including soda, cocoa, dog food,and so on. Specific brand of potato chips which has largest number of customer reviews is
chosen, and it contains 564 reviews.

Selecting single product makes it easy to assume

perceived topics on the point of product features.
First, text mining approach (Netzer et al. 2012) is used to extract useful andmeaningful information, such as terms for products and product features in unstructured text.
The text mining approach helps us reducing the whole vocabulary and avoids unnecessary
computation through two ways, one is that it can automatically disambiguate the words
included in the library which contains frequently used but unimportant words such as I, they,7for, just, cannot, the, these, that, am, is and so on.
uppercase to lowercase.

The other one is its transformation from

Below are two typical examples from the online review data, withthe red words kept as meaningful words after text mining approach. :•Customer A•Helpfulness Denominator: 13. Helpfulness Numerator: 7 (13 − 6).
•Satisfaction Score: 1•Summary: Gross!
•Textual Review: I just cannot understand the high praise these chips have received. I
ordered the variety pack and am very disappointed. All flavors have a really weird
taste to them, except for maybe the plain chips. The salt and vinegar especially tasted
gross! I took one bite and had to throw the bag away. Now I’m stuck with a bunch of
chips that I’ll never eat.

•

Customer B•Helpfulness Denominator: 35. Helpfulness Numerator: 33 (35 − 2).
•Satisfaction Score: 5•Summary: Fantastic chips!!!
•Textual Review: I want to start out by saying that i thought at first that a bag with
only 120 calories and 4 grams of fat (no saturated or trans) for every 20 chips wasgoing to taste like crap. I must say that not only was i wrong, that this is my favorite
BBQ chip on the market today. They are light and you cannot taste any fat or grease
after eating them. That’s because they aren’t baked or fried, just popped as their namesuggests. These chips are very easy to dip as well. FANTASTIC PRODUCT!
8

Customer A gives just a 1-star grade for this product, and 13 viewers regard customer A’s
textual review as helpful, while 6 viewers evaluate it as unhelpful. Customer A questions thehigh praise and rating of the product and criticizes the taste and flavor of the chips. On theother hand, customer B praises the chips with a 5-star rating, and she/he discusses about
ingredients, packing, cooking, taste, and dipping. Thirty-five viewers regard customer B’s
textual review as helpful, and only 2 viewers regard it as unhelpful.

Based on the two

typical examples, we suppose that product features are related with customer satisfaction andproduct expectation as shown in Figure 2, specifically:

Figure 2. Reviews toward Product Features and Helpfulness Recognition•

Experienced customers praise or complain about this brand of chip by talking about
topics about product features that include flavor, taste, ingredients, packing, cooking andconsumption experience, private sentiments, and so on.

•

The readers of reviews mostly have no consumption experience of the chip, and thesenovice customers expect to search for important product information from the reviews
written by experienced customers.

If the review contains informative topics of productfeatures, the readers will recognize the review as helpful.
Our aim is to extract these objective topics from the textual review and explore theconnections between topics and satisfaction or the connections between topics and productexpectation so that online retailers can improve specific product features. We suppose acertain number of perceived topics about food features. Several food features were mentioned9

by scholars, such as culinary quality (Chi et al. 2013), taste and flavor (Andersen and Hyldig2015), service and price (Mason and Nassivera 2013), health/nutrition, and low-fat
ingredients (Küster and Vila 2017). At the same time, in order to find the possible foodfeatures in the reviews, we read the subset of the customer reviews and observe thedescending sort of word frequencies of the whole vocabulary.

The top 60 frequently used

words are listed in Table 1, these feature-based words are about flavor, taste, packing, weight,
food ingredient, cooking method, and purchase.

According to these previous related

literatures and the product features of reviews, we combine synonymic topics and propose
five perceived topics: (1) Flavor and Taste, (2) Packing, (3) Healthy, (4) Money, and (5)Ingredient.

Table 1. Word Frequency of Dataset

3. Model3.1 Supervised Learning Model with Labeled Topics
For the interpretability of the extracted topics in the text analysis, we first employ alabeled topic model. “Labeled” means that some labeled words are fixed to their respective
topics in order to distinguish meanings among latent topics. ,The meanings of extracted
topics are easily interpreted if the synonyms are assigned in common topics, such as“hungry”,”famished” and ”starving” are assigned in a common topic about “hunger”.
Second, we use other information sources for the model to be supervised, where “supervised”
means that the observed numeric data (or ordered categorical data) work as dependent
10

variable, and the frequencies of topics work as covariates.

In this article, we the model by

combining two kinds of models and call it supervised–labeled LDA (SL–LDA) in thefollowing.
Besides textual content, directly observed numerical data, i.e., satisfaction score,
helpfulness numerator, and helpfulness denominator, are available in the data set. The topics
in customer reviews can directly serve as covariates for the variation in satisfaction scoresand helpful reference of product expectation. For example, if one product feature is especiallysatisfying customer needs, we expect that the topic about this feature co-occurs with a highsatisfaction score in online customer reviews. On the contrary, topics about dissatisfying
features should occur along with low satisfaction scores. As for the connection between topic
assignment and product expectation, it is likely that a textual review is regarded as helpful by
readers if it contains expected topics.
Let y d = ( y SA,d , y HN ,d , y HD ,d ) denote the vector of three continuous dependent variables ofcustomer satisfaction, helpfulness numerator and helpfulness denominator in document d.
The satisfaction reflects consumer’s current evaluation based on their past experience, and weassume that helpfulness measures imply their interest and expectation on the product when
they purchase in the future,
Nd

Given the word-topic assignment zi in document d, ∑ # {z i = k } is the number of
i =1words assigned to topic k, and it works as explanatory variables after logarithm
transformation.

We define the supervised model by multivariate regression:yd =X dγ + δd ,d=1,..., D ,where X d = 1 , x1,d x2,d x3,d x4,d x5,d  , and we define the covariate
11

(1)

 Nd

xk ,=
log
k } + 1 ∑ # { z=di i =1
where Nd is the number of words of document d.

(2)

In other words, in order to explain thevariations in satisfaction score, helpfulness numerator, and helpfulness denominator, wequantize the topics about product features with the natural logarithm of topic sizes
Nd∑ # {z = k } + 1 . We assume the error term follows multivariate normal distribution,
i =1

i

(

)

δ d ~ N 3 0, diag (σ 12 , σ 22 , σ 32 ) .

Figure 3. Graphical model

The graphical model of SL-LDA is shown in Figure 3.

In contrast with standard

LDA model, the information of yd is included in the model corresponding to “supervised”.
The core of our modeling is to sample the probabilities with which the word wi is assigned to
topic k, and the probabilities are inferred from the following conditional posterior density :()

p(zi = k | z−i ,θ , φk , yd , wi ,) ∝ p(zi = k | z−i ,θ ) p wi = vλk | zi = k , z−i , φk p( yd | zi = k , z−i , γ , δ ) (3)The detailed process of model estimation is shown in the appendix.

3.2 Transformation Matrix and Labeled Words
We demonstrate how to accomplish the labeled perceived topics by using labeled
words in this section. After removing unnecessary words, we consider the total vocabulary
which includes all words occurred in customer reviews as text data set.

To make topics

interpretable, some meaningful and frequently-used labeled words are chosen from the total
12

vocabulary and fixed in respective perceived topics.

At the same time, the remaining words

which have no definite meaning are called as non-labeled words and assigned to topics withspecific probabilities. For example, in Figure 4, given five topics about product features
and total vocabulary of {v1 , v 2 , v3 , v 4 , v5 , v6 , v7 }, we assume that word v1 works as the labeledword of Topic 1 and it is thus excluded from the other four topics.

Other labeled words

v2,v3,v4, and v5 are specified as the labeled words for the remaining topics. The non-labeled
words v6 and v7, can be assigned to any topic with certain probabilities; hence, the vocabularyof Topic 1 is {v1 , v6 , v7 } , which is the subset of total vocabulary of {v1 , v 2 , v3 , v 4 , v5 , v6 , v7 }.
After establishing vocabulary for topic k, where k=1,2…5,according to LDA model we
expect that customers express the topic k and choose the words from vocabulary of k withspecific probabilities, and the chosen words follow Dirichlet distribution conditional on β*k.

Figure 4. Labeled and non-labeled words for respective topics

In online customer review data or other text analytics, we are faced with huge sized
vocabulary.

To build the vocabulary for topic k from total vocabulary, we imitate andsimplify the methodology from labeled LDA (Ramage et al. 2009).

We first generate thetopic’s transformation matrix Λ(k) (Vk×V) conditional on {λ1 , λ 2 ..λVk }, where {λ1 , λ 2 ..λVk }is the sequence number of probable words.

Just like {v1, v6, v7} for Topic 1 in Figure 4,

Vk=3 and V=7, consequently the choosing of sub-vocabulary is accomplished.

Themechanism of Λ(k) is illustrated in the appendix.
The goal of “Labeled” is making the different vocabularies for each topic through Λ(k),13

in other words, we exclude other topics’ labeled words from the objective topic’s vocabulary,just as we exclude {v 2 , v3 , v 4 } from the vocabulary topic 1 in Figure 4.

In practicalcomputation, some software packages, such as Python, R, and MATLAB, have a filter
function to extract subvectors from the whole vector, so researchers can use these filter
functions to avoid having to make the matrix calculation when Vk and V are especially large.
Given the parameter vector β*k, we sample ϕk, a topic-vocabulary vector and obeys the
Dirichlet distribution:
ϕk ~ Dir(·| β k* )(4)

Similar to collapsed Gibbs sampling of the labeled LDA model (Ramage et al. 2009),
we sample the probabilities in which the topic assignment zi of wi is assigned to topic kaccording to the conditional posterior density:(n + α ) (np (z = k | z , α , β , w) ∝
i

−i

−i
d ,k

*
k

k

−i
k , w = vλk+ β λ*vk

nd + α T ×1 nk + β k*T ×1)(5)where wi = vλk means the word wi is chosen from vocabulary {v1k , v 2k , v3k ,..vλk ..} of topic k.
The term nd−,ik means, except for zi in document d, the number of words assigned to topic k.
The term nk−，i w=v k means, given the topic assignment k, the number of times that vkλ is
λchosen.

In addition, if wi is the labeled word of topic k like v1 in Topic 1, we ignore theequation (5) and assign wi to topic k directly because no Dirichlet prior exists for the othertopic assignments of labeled word wi.

3.3 Model Estimation
We make the SL-LDA model based on two aspects, i.e., one is “supervised” that thenumerical dependent variable yd works as reference information as shown in (3), and the14other is “labeled” that we suppose the perceived labeled topics and labeled words strictly
specified according to prior information about product features as shown in (5).

The

conditional probability density of topic assignment zi = k is given as(n + α ) (np(z = k | w, y, z , α , β *, γ , δ ) ∝
−i
d ,k

−i

i

−i
k , w = vλkk+ β λ*vk

nd + α T ×1 nk + β k*T ×1) p( y | z = k , γ , δ )
d

d ,i

(6),

where i = 1, 2, 3 … , N.
Then, the MCMC process by Gibbs sampling is as follows:(i) Initiate the topic assignment z of both labeled and non-labeled words.
(ii) For each document d = 1, 2 … D, and for each word wi, i = 1, 2, … , Nd. If wi is a
non-labeled word, for topic k = 1, 2, … , K, assign the topic based on (6), else
directly assign the labeled word wi to a certain topic according to step (i).
(iii) Given the topic assignment Z, sample the following posterior mean of the
Dirichlet distribution:

θ d ,k =nd ,k + α k

∑ (n
K

d ,k

+αk )

φ k , w=v =k

λnk , w=v k + β λ*vk

∑ (n
Vk

λ

k , w = vλk+ β λ*vk)(7)(iv) Sample p(γ , δ | z , y ) with Bayesian linear regression and return to step (ii).

4. Empirical Application
Our research object is a brand of potato chips, whose data set has 564 customer
reviews. This data consist of two parts, one of which is textual review and the other isobserved numerical data, including Satisfaction Score, Helpful Numerators, and Helpful
Denominators.

First of all, we need to choose the labeled words for perceived topics

mentioned in section 2.2 appropriately.

We list the word occurrence in descending order,
15

then remove irrelevant words such as “a,” “the,” and “and,” finally selecting the following
labeled words for their respective perceived topics:• Flavor and Taste: flavor, flavors, sweet, salt, taste, pepper, vinegar, tastes, tasty,
delicious• Packing: size, bag, box, bags
• Healthy: calories, calorie, fat, grease, greasy, healthy, health, unhealthy, nutrition,
nutritious• Money: price, money, cheap, expensive• Ingredient: oil, flour, ingredient, ingredients, potato, sugar, starch, flakes
Our model assumes the perceived topics above, meanwhile the labeled words are fixed intheir nominated perceived topics and never switched to other topics, and the remaining words
in total vocabulary will be clustered to the five topics with specific probabilities.

4.1 Word Classification
After MCMC process of Gibbs sampling, which takes 4000 iterations, we illustrate
the word classifications of the top 15 words in Table 2, where we can clearly see that theword classification of SL–LDA has better interpretation than Supervised–LDA (SLDA).
Generally, all five topics of SL-LDA contains corresponding words.

In contrast, the first

four topics of SLDA contain heterogeneous words and are hardly interpreted, only the fifth
topic has clear meaning about food ingredient.

Note that the meanings of topics in SL–LDA

have been presupposed when we appoint labeled words for the topics, so topic names are
directly demonstrated in Table 2.

In contrast, “Topics 1” through “Topic 5” serve as the16topic names in Supervised–LDA since the meaning of most topics is difficult to interpret.

Table 2. Word Classification of SL–LDA and SLDA

The most non-labeled words, just like v6 and v7 in Figure 4, are clustered with related
labeled words and assigned to corresponding topics:•

Flavor and Taste: bbq, favorite, best, better, eating, baked, original•

Packing: weight, single, small, per, ounce, individual, amount, filling, large, larger•

Ingredient: rice, corn, natural, cake, seasoning, heat, sunflower
The name of the topic “Money” was changed to “Money and Buying” because

words such as saving, subscribe, average, reviews, shipping, gives, sampling, alternatives,and extra were assigned to this topic, and these words contain meanings about purchasebehavior.
The topic “Health”, however, presents a problem that the clustered non-labeled
words do not have significant meanings about health, maybe because there is really no otherword about the health topics discussed by customers after 10 labeled words are selected for“Health”.

In addition, many words in “Ingredient” have meanings close to that of “Health”.

We demonstrate the word count for each topic in Figure 5. Apparently, themost-discussed topic is “Flavor and Taste,” which means that most customers are willing totalk about that topic.

Figure 5. Number of words assigned to each topic
17

4.2 Supervised Learning Model: Multivariate Regression
Besides the word classification, our other research purpose is to predict the dependent
variables of customer satisfaction, helpfulness numerator and helpfulness denominatorthrough the interpretable perceived topics about product features.

We use DIC as goodnessof fit to compare the models: (i)“LDA + Multivariate Linear Regression”, (ii) “SLDA”(Supervised LDA), (iii) “SL–LDA” (Supervised–labeled LDA), and (iv) “SL–LDA with
Independent Linear Regression”in Table 3, which demonstrates that SL–LDA has lower DIC,
and it means extracted topics in SL–LDA are quite reliable in interpreting the variation incustomer satisfaction and helpfulness of product expectation.

Table 3. Model Comparison of DIC“LDA + Multivariate Linear Regression” means a separate process that consists of
LDA and multivariate linear regression. Specifically, after topics are assigned by a standard
LDA model, we use multivariate linear regression with {topic1, topic2 … topic5}
frequencies as the explanatory variables.

"SLDA" means that topic assignment is done

according to text data of customer reviews and it connects to numerical variables data in theform of multivariate regression where the {topic1, topic2 … topic5} frequencies defined in(2) serve as the explanatory variables.
In “SL–LDA,” the frequencies of topic assignments about {“Flavor and Taste,”“Packing,” “Health,” “Money and Buying,” and “Ingredient”} work as the explanatory18

variables in step (iv) of MCMC iteration in section 3.3.

The topic assignment is made on

the basis of probabilities generated in equation (6).
“SL–LDA with Independent Linear Regression” means that we execute the SL–LDA
model three times, with {“Flavor and Taste,” “Packing,” “Health,” “Money and Buying,”“Ingredient”} as the explanatory variables, while the Satisfaction Score, Helpfulness
Numerator, and Helpfulness Denominator work as the response variables respectively in thelinear regression.

Furthermore, the “SL–LDA with Independent Linear Regression” will

output three separate groups of word classification, regression estimation, and goodness offit.
We use an HPD region to check the significance of estimated coefficients of intercept
and five perceived topics.

In Table 4, “SL–LDA” and “SL–LDA with Independent Linear

Regression” both output seven significant coefficients (marked with *) compared with threesignificant coefficients in “LDA + Multivariate Linear Regression” and “SLDA”.

After

comparing the DIC and HPD region of four models, we can say that the extracted topics of“SL–LDA” can effectively affect the dependent variables about customer satisfaction andhelpfulness of product expectation, so SL-LDA is the best model in our research.
Specifically, the estimates of SL-LDA shows that “Satisfaction Scores” is positively affectedby the topics of “Flavor and Taste” and “Health,” and negatively affected by “Money andBuying” and “Ingredient.”

Table 4. Estimates of Gamma in Linear Regression (0.1 HPD region test)

19

In the following section, we will have discussion about estimated coefficients of
SL-LDA and topic proportions for all satisfaction levels.

5. Discussion and Managerial Implications
According to the estimated coefficients of SL-LDA, “Satisfaction Score” issignificantly affected by four topics.

Specifically, dissatisfied customers are more likely totalk about “Flavor and Taste” and “Health,” while satisfied customers usually talk about“Money and Buying” and “Ingredient.” We suggest that the manufacturer of these potato
chips improve the attributes of flavor, taste, and health, such as raising awareness andadvertising their nutritive function, so that the needs of dissatisfied customers will be fulfilled.
In addition, reviews including the topic “Health” are regarded as helpful by readers of online
customer reviews, while the topic “Ingredient” weakens helpfulness.

We deduce that novice

customers highly expect the healthiness information from reviews, whereas they pay lessattention to the reviews about ingredient issues, possibly because these readers are impatient
to read the terminology.

Consequently, retailers need to improve the healthy attributes of

their products and advertise healthy attributes to meet novice customers’ expectations.
We simulate the topic assignment for each word in customer reviews through
SL-LDA, so that these customer reviews can be regarded as the mixture comprised with
topics of {“Flavor and Taste,” “Packing,” “Health,” “Money and Buying,” “Ingredient”}, andaccordingly we make the bar chart of topic composition of Figure 6 and Figure 7.
Figure 6 shows the total and average number of words assigned to respective topics ineach satisfaction level.

In the left figure of total word number, we can clearly see that20

satisfied customers are more willing to write online reviews, and the topic “Flavor and Taste”
makes up the most words.

In the right figure about average word number (total number ofwords divided by the number of reviews for the respective satisfaction score level), customers
who gave four scores wrote more words in their reviews, and “Health,” “Ingredient,” and“Packing” receive proportionally more when satisfaction scores increase.

Figure 6. Total and average word number

Figure 7 illustrates the helpfulness degree of product expectation voted by its readers
in different satisfaction levels.

In terms of total quantity (left figure), the most helpful

reviews are made by customers who give 5 stars to goods.

However, individually (rightgraph), reviews made by customers who give just 1 and 2 stars are thought to be more helpful,it means that readers are more likely to search for expected information from the negative
reviews made by dissatisfied customer.

Furthermore, readers care more about “Flavor andTaste,” “Health,” and “Ingredient” when mentioned by dissatisfied customers.

Figure 7. Total and average helpfulness numerator

6. Conclusion
We introduced a supervised labeled LDA (SL-LDA) modelcombined the labelled LDA
to extract

interpretable perceived topics with the supervied LDA model for exploring thestructure of predicting satisfaction of experienced customers and expectation of novice
21customers.

In order to distinguish meanings among latent topics in online customer reviews,

priori labeled words related with product features are assigned to respective topics.
Accordingly, we connect the product features with customer satisfaction as past demand andexpectation as future demand through supervised learning.
According to word classification in section 4.1 and predictive performance in section
4.2, SL-LDA model performs best compared with other models including “LDA +Multivariate Linear Regression”, “SLDA”, and “SL–LDA with Independent Linear
Regression.”.

The research method through which we create perceived topics strictly

according to the prior knowledge about product features is quite effective for improving theinterpretability of topics and goodness of fit for structural model to predict satisfaction and
expectation.
In the empirical study applied to a product of potato chips, we find that “Flavor andTaste,” “Health,” and “Ingredient” mentioned by dissatisfied customers are likely recognised
as helpfulness by review readers, who will click the ”Yes” button if they think the reviews arehelpful for their product expectation, and this public expectation should be a importantreference about word-of-mouth improvement for marketers.

“Flavor and Taste” and“Health” negatively affect satisfaction, so retailers should track the details of the two productfeatures to find what is complained about by dissatisfied customers.
Two characteristics of online customer reviews help us to employ the priorinformation and enhance the predictive fit of our model.

One is that customers discuss

product features with concise textual reviews without deep semanteme such as slang andadage.

The other one is the simple and convenient extraction of features from single
22

product, thus the perceived topics about product features are easily artificially presupposed,
otherwise the complex features of heterogeneous products will cause great difficulties forchoosing perceived topics.

In managerial application, as long as the retailers take advantageof the prior information and presuppose perceived topics properly, the SL-LDA model shouldmake good predictive fit and help retailers making textual analytics accurately.
Future research is needed in two aspects about marketing improvement.

One is the

improvement of product feature analytics through evolutive LDA models or other linguistic
methods, such as employing word sequence, linguistic structure and word nature as more
informative “labeled”.

Another aspect is using the topic model to predict more “supervised”

objectives such as detecting fake or deceptive reviews, in which meaningless reviews andhigh score rating are made after false purchase behavior in P2P e-shop.

23

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26

Appendix
A.1 Graphic Model of SL–LDA

Let k ∈ {1, . . . , K} and d ∈ {1, . . . , D}, where K and D mean the number of
topics and documents (customer reviews), respectively, and Nd stands for the number ofwords in document d. α is a K-dimensional vector acting as the Dirichlet prior for each row of
θ, where θ is the D×K dimensional document-topic matrix defining mixing proportion.

(

Denote β = (β1 , β 2 ...β V ) and β k* = β λ1, k , β λ2 , k ...β λvk ...β λVK) as V and V dimensional vectors,krespectively, where V is the size of the total vocabulary. Vk is the size of vocabulary{v , v ,...v ,...v ,} for topic k, and Vk < V. Λk is defined as the transformation matrix for the
k
1

k
2

k

λ

k
Vk

reducing dimension of topic k. φ = {φ1 , φ 2 ,...φ k ...φ K }, where ϕk means the Vk dimensional
topic-vocabulary vector. The term w = (w1, w2, … wNd) is the occurred words of document d.
z = (z1, z2, … zNd), zi is the word-topic assignment of word wi.

27

A.2 Transformation Matrix of Labeled LDA
In Figure 4, the specific form of Λ(k) is as under.
For each row i ∈ {1,2...Vk } and column j ∈ {1,2...V } : 1 if λi = j
Λ(ik, )j = 0 otherwise(A1)

Hence, the parameter vector of Dirichlet prior β is transformed to a lower dimensional
vector β*k.
β k* = Λ( k ) β β1 
 β λ1  β2 
β 
 β λ2 
 3
*
(k )


β
β k =  λ3  = Λ  β 4 β  ...  5β 
 ...  λVk 
 βV 

(A2)

Just as in the example in Figure 4, the sequence number of probable words for Topic 1
is {λ1 , λ 2 , λ3 } = {1, 6, 7}; in other words, {v1 , v6 , v7 } are chosen as potential words of topic k,
and the dimension reduction process is as follows: β1   1  
β =  β6  = 0
 β  0
 7 
*
1

0
0
0

0
0
0

0
0
0

0
0
0

0
1
0

β 
 1
 β2 
0 β3  
0  β4 
1   β 5 
 β6 
 
 β7 (A3)

A.3 Supervised–LDA
To build the conditional likelihood p( y | z , γ , δ ) , multivariate linear regression isapplied as under.
For the document d = 1, 2,..., D ,28=yd X d γ + δ d ,γ SA,0 γ HN ,0 γ HD ,0 γ SA,1 γ HN ,1 γ HD ,1 
γ SA,2 γ HN ,2 γ HD ,2  ySA,d yHN ,d yHD ,d  1 x1,d x2,d x3,d x4,d x5,d  =
 + [δ SAd δ HNd δ HDd ]




 
 γ SA,3 γ HN ,3 γ HD ,3  δdyd
Xdγ SA,4 γ HN ,4 γ HD ,4 
γ SA,5 γ HN ,5 γ HD ,5 


(A3)

γ

A.4 Bayesian Inference of SL–LDA
Our purpose is to obtain the following conditional posterior:()

p(z i = k | z −i ,θ , φ k , y d , wi ,) ∝ p(z i = k | z −i ,θ ) p wi = vλk | z i = k , z −i , φ k p( y d | z i = k , z −i , γ , δ ) (A4) 
part1

part 2

Part 1 is about the text data of labeled LDA, and Part 2 is about the numerical data ofsupervised–LDA. First, we build the joint distribution according to our graphic model:p(w, z , θ , φ , y | α , β *, γ , δ ) = p(θ | α ) ⋅ p(z | θ ) ⋅ p(φ | β *) ⋅ p(w | z , φ z ) ⋅ p( y | z , γ , δ ) (A5) 
part 2

part1

Identical with a collapsed Gibbs sampling in a general LDA model, we can bypass theparameters of Φ and θ in Part 1 and make the specific form of joint distribution as follows:p(z, w, y | α , β *, γ , δ )= ∫∫ p(w, z,θ , φ , y | α , β *, γ , δ )dθ dφ= ∫ p(θ | α ) ⋅ p(z | θ )dθ ⋅ ∫ p(φ | β *)p(w | z, φ z )dφ p( y | z, γ , δ )

(

)

(A6)




Γ
Γ
*
α
β



(
)
Γ
+
Γ
n
n
α
k 
k + β *λ
∑
∑
k
∏
∏
,
d
k
k
=
w
v
D
K
D
k , w = vλ
vk
λ
w
⋅ w
=∏  k ⋅ kp( y d | z d , γ , δ )∏∏ k =1 ∏ Γ β *w=vλk
 d =1 ∏ Γ(α k )
d =1




Γ ∑ n d , k + α k Γ ∑ nk ,w=v k + β *λvk kw
λpart 2k

w


(

)

part 1

Because Φ and θ both disappear in the end of (A6), the collapsed Gibbs sampling isavailable in Part 1. Part 2 is the conditional likelihood of (A3), in which yd obeys multivariate
normal distribution. Given the above joint distribution function, we iterate conditional densityof zi conditional on {z1, z2, … zi − 1, zi + 1, … zN,} in collapsed Gibbs sampling, where i = 1, 2,
29

3 … N:p( z i = k | w, y, z −i , α , β *, γ , δ )=p( z i = k , z −i , w, y | α , β *, γ , δ )p( z −i , w, y | α , β *, γ , δ )∝ p( z i = k , z −i , w, y | α , β *, γ , δ )

(

(A7)

)

∝ p( z i = k , z −i | α ) p wi = vλ , w−i | z i = k , z −i , β p( y | z i = k , z −i , γ , δ )
∝

(n

−i
d ,k

+αk

) (n

−i
k , w = vλkk+ β λvk**k) ⋅ p( y | z , γ , δ )nd + α × 1 nk + β k × 1 
part 2

T

*T

d

d ,i

part 1

Then, the conditional posterior probability isp(z i = k | w, y, z −i , α , β *, γ , δ ) = K

p(z i = k | w, y, z −i , α , β *, γ , δ )∑ p(z = j | w, y, z ,α , β *, γ , δ )
−i

i

j =1

(n + α ) (n
−i
d ,k

k

−i
k , w= vλk) ⋅ p( y | z = k , γ ,δ ) (A8)+ β λvk *nd + α T × 1 nk + β k*T × 1=−iK
nd−,i j + α j n j , w=v j + β λvj *)((∑ n + α ×1
j =1

T

d

λ

n j + β *j × 1
Tdi) ⋅ p( y | z = j, γ , δ )diFinally, we sample the topic assignment zi according to the posterior conditional
probability for each probable topic in the MCMC process.

30

Table 1. Word Frequency of Dataset

Frequency
Word Frequency Word Frequency
Word
chips
760
regular117
original
63
flavor353
try112
texture62
like345
vinegar108
something60
potato
300
eat
107
time
59
taste
281
tried
106
crunchy59
popchips
243
much
106
product
57
bag
232
salty
99
lime
57
good
232
favorite
93
light
56
great
212
best
93
box
56
love
209
get
91
bought53
sweet
196
healthy84
weight53
salt
194
better
82
chili
53
chip
170
eating81
crunch52
really
137
pop
80
tasty
52
fat
136
little80
definitely
52
snack
130
buy
75
fried
52
calories
127
baked
71
case
51
bags
123
low
70
cheddar51
flavors119
pepper
69
rice
49
bbq
119
find
65
food
49

31

Table 2. Word Classification of SL–LDA and SLDA
SL-LDA
Flavor and
Taste
chips
flavor
like
taste
good
salt
popchips
love
great
reallyflavors
tried
much
try
bbq

Packing

Health

bag
bags
box
snack
chips
size
popchips
weightgreat
single
love
eat
small
per
ounce

Topic 1snack
calories
makes
perfectbag
popchips
relatively
afternoon
gives
each
mine
shipments
compared
boxes
tasty

Topic 2
sour
cream
onion
cheddarafter
originalgarlicreadingparmesanidea
reviews
thickerhelps
preferencebagbr

calories
fathealthychip
lowcaloriegreasysnack
chips
crunchcrunchygrams
sodium
great
salty
SLDA
Topic 3chips
flavorlike
taste
potatogood
love
popchips
bag
great
salt
chip
reallytried
flavors

32

Money andBuying
price
save
money
after
subscribe
expensive
readingaveragereviews
thembrhelps
thickershipping
decidedseems
Topic 4weightwatchers
pointschips
portion
love
bags
great
bag
single
low
serve
pop
controlpopchips

Ingredients
potatorice
ingredientsoilpotatoesflour
corn
naturalstarchingredient
cake
list
flakessugar
seasoning
Topic 5flour
rice
ingredientscorn
oilstarchingredientnaturalflakessugar
safflower
sodiumseasoning
list
preservatives

Table 3.Model Comparison of DIC
Model

DIC

LDA + Multivariate Linear Regression

2707.46SLDA
SL–LDA

2611.38186.66

SL–LDA with Independent Linear Regression

1559.92 + 1605.84 + 1603.81

Table 4. Estimates of Gamma in Linear Regression (0.1 HPD region test)
Explanatory
VariableIntercept
γ1
γ2
γ3
γ4
γ5
Intercept
γ1
γ2
γ3
γ4
γ5
Intercept
γ1
γ2
γ3
γ4
γ5

Response
Variable

Satisfaction
ScoresHelpfulness
Numerator

Helpfulness
Denominator

LDA +Multivariate
Linear Regression

SLDA

SL-LDA

SL-LDA with
Independent Linear
Regression−0.104081−0.082277*
0.025689−0.047175
0.016622
0.202006−0.465789−0.013201*
0.152468
0.048836
0.007903
0.096529−0.296761
0.030989
0.101681
0.039018*−0.047418
0.021323−0.16475−0.45349*−0.27023
0.14893−0.0499−0.09601−0.02932−0.0463−0.0601
0.04158
0.14977*
0.094860.23187*
0.107020.0237−0.06036
0.04784
0.04359

0.17948−0.35696*−0.26295−0.16791*
0.12293*0.11739*0.17587*
0.0261−0.02763
0.26881*−0.02411−0.10661*−0.05071−0.01633
0.05048−0.07703
0.05149
0.068810.17365−0.17229*
0.18504*
0.25785*−0.03308−0.06994−0.33906*
0.11597*
0.03422−0.03415−0.01212
0.05451−0.26540.11932*−0.02704−0.10624*
0.03750.0656933

Figure 1. Layout form of Amazon food review

Figure 2. Reviews toward Product Features and Helpfulness Recognition

34

Figure 3. Graphical model

Figure 4. Labeled and non-labeled words for respective topics

v1

v2

v3

v4

v5 v 6

Topic 1, β1*  β1
Topic 2, β 2*  ×Topic 3, β 3*  ×Topic 4, β 4*  ×Topic 5, β 5*  ×

×

×

×

×

β2

×

×

×

×

β3

×

×

×

×

β4

×

×

×

×

β5

35

β6
β6
β6
β6
β6

v7

β7 
β 7 β7 
β7 
β 7 

Figure 5. Number of words assigned to each topic

Figure 6. Total and average word number

Horizontal axis：satisfaction score. Vertical axis: number of words

36

Figure 7. Total and verage helpfulness numerator.

Horizontal axis：satisfaction score. Vertical axis: number of helpfulness numerator.

37