Discussion Paper No. 112
Interpretable Perceived Topics in Online Customer
Reviews for Product Satisfaction and Reader
Helpfulness

Mirai Igarashi, Aijing Xing
and Nobuhiko Terui
April, 2020

 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 Reader
Helpfulness
Mirai Igarashi*

Aijing Xing

Nobuhiko Terui

April 2020

* Mirai Igarashi is Doctoral Student, Tohoku University, Graduate School of Economics and Management,

27-1, Kawauchi Aoba-ku, Sendai, 980-8576, Japan (E-mail: mirai.igarashi.s7@dc.tohoku.ac.jp). Igarashi
acknowledges a grant by JSPS KAKENHI 18J20698.
 Aijing Xing is Algorithm Engineer, Alibaba Group, Ant Finance of Insurance, Business Data Department
Z-Space, Xihu District, Hangzhou, Zhejiang Province, 310013, China (E-mail: sdxingaijing@sina.com).
Nobuhiko Terui is Professor, Tohoku University, Graduate School of Economics and Management (Email: terui@tohoku.ac.jp). Terui acknowledges a grant by JSPS KAKENHI (A) 17H01001.

Interpretable Perceived Topics in Online Customer
Reviews for Product Satisfaction and Reader
Helpfulness
Abstract
Online customer reviews contain useful and important information, particularly, for
product development and management, because customers praise or criticize in theirreviews certain product attributes. We propose a model that extracts perceived topics from textual reviews using natural language processing under some restrictions
created using seed words for improving the topic interpretability. In addition, theproposed model estimates the relationships between the topics and product satisfaction by writers of the review and the perceived helpfulness of reviews by readers, thatis, these textual reviews are viewed as current product evaluations by customers who
have made purchases and expectations of possible future demand by consumers who
have yet to make purchases. The empirical study on e-commerce food reviews showsthat our proposed model performs better than the extant alternative models and provides interesting ﬁndings such that the “ingredient” topic in the review text decreases
the levels of customer satisfaction and the reader’s perceived helpfulness. In contrast,
the “health” topic increases the levels of both customer satisfaction and the reader’s
perceived helpfulness. These ﬁndings help us understand the product attributes thatpurchased customers are satisﬁed with and for which readers of reviews ﬁnd helpful
information.
Keywords: Customer review analysis, Preference measurement, Food satisfaction,
Perceived helpfulness, Latent Dirichlet allocation, Supervised learning, Bayesian inference, User-generated-content

1Introduction

Given the increase in electronic commerce, online retailers such as Amazon, Walmart, andTaobao have experienced growth in their number of users. Most online retailers have customer feedback systems to receive information for customer reviews, including satisfaction
scores (also called product ratings), textual reviews, and readers helpfulness. In customer
reviews, customers argue about their experiences and evaluations of product attributes.
Company and marketer must clarify the perceived quality for products and estimate
their preferences for or importance of each product attribute because this information canbe utilized to discuss and conduct vast marketing strategies for product development, brandpositioning, and advertising. Berger et al. (2020) suggested that companies and marketers
can use online reviews to understand whether consumers prefer a product, how consumers
feel about a brand, the attributes that are relevant for decision making, or the other brands
that fall into the same consideration set.
Customer reviews not only describe customers’ evaluations and experiences with a product but also provide information about the product for consumers who read reviews to makepurchasing decisions. Chen and Xie (2008) suggested that online reviews can help novice
consumers identify products that best match their preferences. These authors concluded
that, in the absence of information from reviews, novice consumers may be less likely to buy
a product if the only information available is seller-created product attributes. Obviously,
customers prefer to read customer reviews before making purchase decisions because suchuser-generated information is perceived to be trustworthy and in line with their preferences.
A large and growing body of literature has investigated methods for measuring the perceived quality of products, and these methods are related to customer surveys or interviews.
However, information is obtained through surveys and interviews at a time lag, tends tobe limited, and is costly. Additionally, to create a questionnaire, product attributes need
to be selected from a pre-deﬁned set of attributes; that is, such attributes are companyoriented. In contrast, information obtained from online customer reviews is instantaneous1and tends to be high in volume and low cost. In addition, these product attributes are
selected by customers. In other words, they are customer-oriented. Therefore, for companies
and marketers to utilize customer review data and estimate customer preferences or readers’perceived helpfulness is useful.
In the literature, several methods for preference measurement and market research using
online customer reviews have been proposed (e.g., Decker and Trusov, 2010). To analyze
customer reviews, we ﬁrst need to extract the product attributes mentioned in the reviews.
These studies adopted a rule-based approach that one-to-one translates from words or phrases
to product attributes. Through this approach, researchers create lexicons for translation byhumans or by using useful tools, such as machine learning, and then map words or phrases
to product attributes using the lexicons. Then, they construct a statistical model to explain
the relationships between the quantiﬁed review text and the dependent variables, such as
customer satisfaction.
However, through this approach, which engages in a one-to-one translation of words toattributes, the important information from the contexts of words meanings and sentiments isignored. For example, “small” could be a positive attribute for a mobile phone and a negative
attribute when describing a screen. Some studies proposed a topic modeling approach (e.g.,
Tirunillai and Tellis, 2014). The topic modeling approach assumes that each word may be
assigned to multiple topics (or product attributes) according to the context and can ﬂexibly
extract product attributes from review text. In addition, because the approach involves
little human intervention, the researchers do not need to know the latent product attributedimensions in advance, and human error and biases can be minimized as much as possible.
However, the topic modeling approach is problematic because there is no guarantee that
we always extract interpretable topics. Basically, the topic modeling approach is based onlatent Dirichlet allocation (LDA) as proposed by Blei et al. (2003), and the core of the
LDA model is the unsupervised learning method, that is, no word is given any correct topic
under supervision and is assigned to the most likely topic in the learning process. Because2the assignment depends entirely on unsupervised learning, a set of words may be obtainedwith cohesion that is incomprehensible to human. As a result, even if researchers use thetopic modeling approach to extract product attributes in customer reviews, they might notachieve preference measurements—the ultimate purpose—if they cannot understand what
the attribute actually represents.
In this study, we propose a semi-supervised topic modeling approach to tackle the problem. This approach provides to the topic model symbolic words that are representative of a
product attribute, which is quite diﬀerent from the original topic model that does not oﬀer
any prior knowledge. For example, regarding a product’s price attribute, “expensive” and“cheap” can be vied as representative words for that attribute. The topic assignments of thesewords are ﬁxed to corresponding topics. The proposed topic model considers some labeled
words whose topic assignments are determined in advance and assigns other non-labeled
words to relevant topics based on the labeled words. Therefore, the topics resulting fromthe analysis contain several pre-assigned representative words, and the proposed approach is
expected to improve the interpretability of topics relative to the original unsupervised topic
modeling approach. Furthermore, to not require prior knowledge of the analyst is one of thestrong points of the topic modeling approach. However, this advantage is wasted if the analyst had considerable knowledge of the domain. In contrast, the proposed semi-supervised
approach can naturally incorporate into the model such prior knowledge of the analyst.
The purpose of this research is to propose a topic model that stably extracts interpretableproduct attributes from customer reviews by providing pre-deﬁned word labels and to answer
the following research questions using the proposed model: “What product attributes are
mentioned in the online customer reviews and what is the relationship between the productattributes and purchasing customers’ satisfaction?” and “What product attributes do readersof customer reviews expect to ﬁnd as useful information?”In this empirical study, we use Amazon customer review data to demonstrate the performance of the proposed model and provide a case study that applies our model to a real3

dataset. First, we compare the proposed model with existing models from two viewpoints:
model selection via information criteria and interpretability of the extracted topics. We use
these comparisons to show that diﬀerences between our model with the word labeling restriction and the no restriction model are not very large and that our model extracts topics
that can be more easily interpreted. Next, we discuss the estimates to obtain interestingﬁndings, such that the “ingredient” topic in the review text decreases the levels of customer satisfaction and the perceived helpfulness of readers, and in contrast, “health” topic
increases the levels of both. However, in this study, we use the customer review dataset for
only one product, which is a limitation of this work. Because our research is only a casestudy, generalizing the ﬁndings and applying our model to other products and categories isnecessary.
In Section 2, we discuss related works in the relevant body of literature. In Section 3,
we describe the details of dataset used in this study and how to construct “labeled” topics.
Then, in Section 4, we propose the supervised-labeled LDA model and derive the inference
algorithm. Section 5 presents an application of the proposed model for Amazon customer
review data and a discussion of the results. Finally, in Section 6, we provide concluding
remarks and directions for future research.

2

Literature Review

2.1

Customer Review Analysis for Customer Satisfaction and Reader
Helpfulness

This section provides a brief overview of related studies on customer review analysis. First,
we discuss several studies that aimed to capture the feelings that customers expressed intheir reviews. In the ﬁelds of marketing and management, researchers proposed several
approaches to extract the product attributes mentioned in customer reviews and estimate
their relationships with customer satisfaction and product sales. Decker and Trusov (2010)4and Xiao et al. (2016) estimated the impact of product attributes that were positively andnegatively mentioned in ratings using a latent class Poisson regression and heterogeneous
multinomial choice model. Archak et al. (2011) proposed a demand estimation model that
captures the eﬀects of product attributes on sales considering the heterogeneity of each wordthat qualiﬁes the attributes.
These studies treated all reviews equally as subjects for the analysis. However, Qi et al.
(2016) used human and machine learning methods to select only useful reviews and to analyze
the eﬀects of attributes on product ratings. Companies and marketers can use the relationship between product attributes and customer satisfaction and sales to analyze the market
structure (Lee and Bradlow, 2011; Netzer et al., 2012) and improve the search algorithm forproduct pages (Ghose et al., 2012).
Customer reviews contain assessments of product attributes by customers who have already purchased the product and aﬀect the perceived helpfulness of consumers who arepotential buyers. Some studies analyzed the relationship between the review attributes andreaders’ helpfulness. For example, Chen and Xie (2008) explored the interactive eﬀects ofseller-created product attributes and buyer-created review information on readers’ usage experiences. They clariﬁed that customer reviews help consumers identify products that best
match their idiosyncratic usage conditions.
Hence, consumers are observed to read reviews in search of useful information. Yin
et al. (2017) analyzed the helpfulness that readers receive from customer reviews by taking
into account the variations in the degrees of emotional arousal. Additionally, Mudambi and
Schuﬀ (2010) revealed the factors that make reviews helpful to customers during the purchasedecision process and described the eﬀect of review ratings, review depth, and product types,
search and experience goods. In this study, we build on these previous studies and propose
a model that identiﬁes the product attributes that make reviews helpful.

5

2.2

Extracting Product Attributes from Customer Reviews

The approaches for analyzing customer reviews to reveal the relationship between the productattributes mentioned therein and the dependent variables, such as satisfaction, sales, andreader helpfulness, can be broadly stated as being a two-step process that extracts productattributes from review text and constructs a statistical model to estimate the eﬀect of productattributes on dependent variables. In this study, we focus on improving approaches for
extracting product attributes from review text and start with identifying how the proposed
methods extract attributes and their limitations.
Most of the studies previously discussed adopted a rule-based approach that maps words
and product attributes on a one-to-one basis. After preprocessing the review text, including
removing stop words and word stemming, these studies created rules that determined the
product attribute that a word represents using either human power (Moon and Kamakura,
2017; Hou et al., 2019) or machine learning techniques, such as clustering (Lee and Bradlow,
2011; Archak et al., 2011). By identifying the correspondence between words and the attributes, unstructured text data can be transformed into quantiﬁed variables, and researchers
can explore the eﬀects of attributes on the dependent variables through a regression or choicemodel.
However, these rule-based approaches, which transform words into attributes one-toone, ignore important information, ﬂuctuations in the meanings and sentiments of words
according to the context, similar to the example of “small” in Section 1. To capture these
ﬂuctuations, approaches using topic modeling, especially LDA (Blei et al., 2003), have been
proposed in the literature. The LDA model reﬂects the generative process of text, and aword is assumed to be generated from the vocabulary on its latent topic. The characteristicof the LDA is that topics may diﬀer according to the context, even if it is about the sameword.
In customer review analysis, the topics in the review text can be treated as productattributes, and some studies used the LDA model and proposed an extension model to6extract product attributes from the review text. For example, Tirunillai and Tellis (2014)
proposed an extension of the LDA model by adding a mechanism that considers the latentsentiment of words. They extracted the key latent dimensions of consumer satisfaction to
conduct brand positioning analysis. Büschken and Allenby (2016) proposed a topic modelthat considers sentence-based topics rather than independently assigning words to topics toanalyze the importance of each product attribute to customer satisfaction.
As brieﬂy discussed in previous sections, these topic modeling approaches also have limitations in terms of interpretability. Because a topic model is based on unsupervised learning
for topic assignments to words, some words with less semantic cohesion may be clustered ina topic. This clustering leads to a meaningless analysis of the relationship between productattributes and satisfaction or helpfulness, which is the ultimate goal of using the topic model.
In this study, we propose a semi-supervised topic model that provides representative
words for a product attribute as supervision. The model can also assign topics to eachword based on these seed words. Similar approaches using seed words or seed labels havebeen proposed in the literature. Lin et al. (2012) and Tirunillai and Tellis (2014) used theseed words approach to estimate word sentiments by ﬁxing some word sentiments to either
polarity, such as good and great as positive words, and bad and terrible as negative words.
Additionally, Ramage et al. (2009) proposed for the labeled LDA model that some tags
previously assigned to documents are considered to be seed topics, and the word topics inthe documents are determined from the set of seed topics. Therefore, the proposed model
can be said to be an application of the approach used in these previous studies to assign
topics to words.
In this study, we adopt the supervised topic model (Blei and McAuliﬀe, 2007) to estimate the relationships among product attributes extracted by the labeled LDA model andcustomer satisfaction and reader helpfulness. Some studies integrated the text information
quantiﬁed by the LDA into regression models, such as linear regression and multinomial probit, to treat the topic extraction part and the regression part as separate models (Qi et al.,7

2016; Bi et al., 2019), whereas others treated them together as a single model (Büschken
and Allenby, 2016; Puranam et al., 2017). We take the latter position but compare both
approaches in the section of the empirical analysis.

3

Data

In this section, we explain the details of the dataset used in this study and construct “labeled”
topics. We use an Amazon customer review dataset collected by the authors that consists of
1,178 customer reviews on a speciﬁc potato chip product on Amazon platform from March2009 to October 2019. Selecting a single product facilitates the assumption of perceived
topics on the point of product features.
This dataset includes ﬁve variables, review texts, product rating scores, voted helpfulness
counts, and some control variables. Speciﬁcally, after buying and accepting goods, customers
are allowed to post their feelings and experiences by writing a textual review. At the sametime, these customers may grade products by assigning “star” ratings from 1 to 5. The
reader of this online review can evaluate whether the review is helpful to his or her productconsideration and purchase decision by clicking on the “helpfulness” button. As control
variables, we consider four types of status badges: purchase veriﬁcation, top contributor,
top reviewer, and vine voice. Purchase veriﬁcation indicates that the customer who wrote
the review was veriﬁed for purchasing on the Amazon platform. The top contributor badge
is awarded to customers who frequently share reviews or answers questions from other customers. Top reviewers are identiﬁed by Amazon’s reviewer rankings, but the ranking system
algorithm is not clariﬁed. Vine voice is an invitation program that gives Amazon reviewers
advance access to not-yet-released products for the purpose of writing reviews. Therefore,
these variables also may have some impact on product ratings and readers’ attitudes, as wellas perceived topics. In the next section, we construct a statistical model to unveil these
relationships.

8

Before the data analysis, review texts were preprocessed. First, for each document, we
split the text into word sets and substituted capital letters with lower-case ones. Next, we
excluded numbers, punctuation, and popular stop words (e.g., a, the, I, they). We alsotransformed inﬂected words to their word stems to reduce the redundancy of the created
corpus. Finally, we excluded frequently used and rare words because they adversely aﬀect
the extraction of topics or the interpretation of the topics after extraction. As a result, thecreated corpus included 716 unique words, and each review consisted of an average of 9.8
words.
The purpose of this study is to extract objective topics from the textual review andexplore the connections among the topics, review writers’ satisfaction, and readers’ perceived
helpfulness. This study aims to assist online retailers and marketers in developing andimproving their products according to speciﬁc discovered relationships between objective
product features and satisfaction or helpfulness.
To extract objective topics, we assume a certain number of perceived topics on featuresof food. Scholars mentioned several features of food, such as culinary quality (Chi et al.,2013), taste and ﬂavor (Andersen and Hyldig, 2015), service and price (Mason and Nassivera,2013), health/nutrition, and low-fat ingredients (Küster and Vila, 2017). At the same time,
to identify the possible features of food in the dataset, we read subsets of customer reviews
and observe the frequently used words of the entire vocabulary. Based on observations,the features of food are related to ﬂavor, taste, packing, weight, food ingredients, cooking
method, and purchases. According to the previous related literature and the observed product features in the dataset, we combine synonym topics and propose ﬁve perceived topics:(1) ﬂavor and taste, (2) packing, (3) healthy, (4) money and buying, and (5) ingredient. In
this study, we need to select seed words for each labeled topic, and these words should besymbolic and representative of each product feature. We used the literature and the subset
of customer reviews to select the seed words for each labeled topic. Table 1 provides a listof labeled words.

9

salt, vinegar, ﬂavor, cheddar, test, bbq,
crunchy, texture, tasty, sweet, pepper, salty
Packing: case, box, product, store, bag, pack, weight
Healthy: healthy, fat, calorie
Money and Buying: try, buy, get, bought, ﬁnd, price
Ingredient: oil, ﬂour, ingredient, starch, ﬂake
Flavor and Taste:

Table 1: List of labeled words for each product feature

4

Model

4.1

Supervised Learning Model with Labeled Topics

To improve the interpretability of the extracted topics in the text analysis, we ﬁrst employ a labeled topic model. “Labeled” means that some labeled words are ﬁxed to theirrespective topics to distinguish meanings among latent topics, where topics represent product attributes in customer reviews. The meanings of extracted topics are easily interpreted
if the synonyms are assigned in common topics. For example, “hungry,” “famished,” and“starving” are assigned to the common topic “hunger.” Second, to supervise the model, we
use other information sources, where “supervise” means that the observed numeric data—
including ordered categorical satisfaction score and positive integer helpfulness count—work
as dependent variables, and the frequencies of the topics in customer review are covariates.
In this article, we propose the supervised-labeled LDA (SL-LDA) model, which is the combination of two types of models, the LDA model with labeled words and the supervised topic
model.
First, we demonstrate how to accomplish the labeled perceived topics using labeled words.
After removing unnecessary words, we consider the total vocabulary, which includes all words
in customer reviews as text content. To make topics interpretable, some meaningful andfrequently used labeled words are selected from the total vocabulary and ﬁxed in respective
perceived topics, as discussed in the previous section. Simultaneously, remaining words with

10

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Figure 1: Labeled and non-labeled words for each topics

no pre-deﬁned labels are called non-labeled words and assigned to topics according to a
speciﬁc distribution.
Figure 1 provides an example of constructing labeled topics. Given ﬁve topics on productattributes and the total vocabulary of v1 , v2 , v3 , v4 , v5 , v6 , and v7 , we assume that word
v1 works as the labeled word of topic 1 and, thus, is excluded from the vocabularies of theother four topics. Other labeled words, v2 , v3 , v4 , and v5 , are speciﬁed as labeled words forthe remaining topics. The non-labeled words, v6 and v7 , can be assigned to any topic witha certain probability. After establishing the vocabulary for topic k, where k = 1, 2, . . ., 5
according to the LDA model, we expect that customers choose words from the vocabulary of
topic k with speciﬁc probabilities to express their feelings for the product attribute of topick.
To develop the vocabulary for topic k from the total vocabulary, we imitate and simplify
the methodology from the literature, such as labeled LDA (Ramage et al., 2009) and joint
sentiment LDA (Lin et al., 2012; Tirunillai and Tellis, 2014). We ﬁrst generate the topic’s
transformation matrix Λ(k) (Vk × V ) conditional on λ1 , λ2 , . . ., λVk , where λ1 , λ2 , . . ., λVk isthe sequence of the number of labeled words for topic k and non-labeled words. Similar to1, 6, and 7 for topic 1 in Figure 1, Vk = 3 and V = 7. For each row i ∈ {1, 2, . . . , Vk } and11

column j ∈ {1, 2, . . . , V },(k)Λi,j =⎧⎪⎪⎨1 if λi = j⎪⎪⎩0 otherwise.

(1)

In this model, the topic-vocabulary vector φk , called word distribution, is assumed to follow the Dirichlet distribution, φk ∼ Dirichlet(βk∗ ), where βk∗ is transformed from the Vdimensional hyper parameter β via the transformation matrix, βk∗ = Λ(k) β. Hence, thelength of βk∗ is Vk .
The remaining Labeled LDA part of the proposed model is similar to the LDA model.
That is, the topic assignment for the n-th word in review d is assumed to follow the categorical
distribution, zdn ∼ Categorical(θd ), where θd is a topic distribution for review d and follows
the Dirichlet prior distribution, θd ∼ Dirichlet(α). We assume that a word assigned to topic
k is generated from the corresponding word distribution, wdn | zdn = k ∼ Categorical(φk ).
Next, we develop the “supervised” part of our model. In this model, we use two dependent
variables: satisfaction score and helpfulness count. The satisfaction score reﬂects the current
evaluation by customers who already purchased the product based on their past experiences,and we assume that helpfulness counts imply the interest and expectations of the productby other consumers who may purchase it in the future. Additionally, as discussed in theprevious section, the labeled topics in customer reviews can directly serve as covariates forthe variations in the satisfaction scores and as helpful product expectation references. For
example, if one product feature is particularly satisfying to customer needs, we expect thatthe topic related to this feature co-occurs with a high satisfaction score in online customer
reviews. In contrast, topics related to dissatisfying features should occur with low satisfaction
scores. Regarding the connection between topic assignment and review helpfulness, a textual
review is likely to be regarded as helpful by readers if it contains the topics in which they
are interested.

12Given the word-topic assignment, zdn , Ndl = Nd

n=1 I{zdn = l} is the number of words

assigned to topic l and works as explanatory variables after a logarithm transformation.
Note that the number of explanatory variables for these topic assignments is the same as thenumber of labeled topics, L (set to ﬁve in this study). However, the number of total topics
can be diﬀerent from the number of labeled topics. Let the number of total topics be K, andthe rest of the topics, K − L, are non-labeled and do not include any labeled words. Because
non-labeled topics are extracted from the review text but not considered to be explanatory
variables, they work as topics for collecting outlier words or topics in the text and do notaﬀect the following regression analysis even if their cohesion and meanings are diﬃcult forhumans to interpret.
The number of total topics, K, is determined by comparing models with varying numbers using deviance information criterion (DIC) and widely applicable information criterion(WAIC), as is demonstrated in Section 5. In addition to the topic assignment variables, some
control variables also work as explanatory variables. In this study, we consider the following
four status variables: purchase veriﬁcation, top contributor, top reviewer, and vine voice.
The status badges are displayed next to the user icon if the user qualiﬁes for the status.
Word count variables, which represent the number of words included in the customer review,are also considered.
We assume that satisfaction scores measured using ﬁve point scales follow the ordered
probit model, and helpfulness counts that are positive integers follow the Poisson regression
model. First, let the satisfaction score of review d be ys,d , which follows the ordered probit
model:

ys,d = r,
∗
=
ys,d

L


∗
if τr−1 ≤ ys,d< τrγs,l · log (Ndl + 1) +

(2)
5


δs,m · xs,dm + d ,

(3)

m=1

l=1

where the thresholds {τr } work for realizing discrete satisfaction scores through the latent13

∗continuous variable, ys,d, and thresholds τ0 and τR (R = 5 in Amazon data set) are set to−∞ and ∞, respectively. xs,d is a vector of the control variables—status variables and word
counts. Additionally, to identify the SL-LDA model, the error termd is assumed to follow

the standard normal distribution and does not include the intercept term. Therefore, we canfreely estimate the remaining R − 1 thresholds. Next, let the helpfulness count for review dbe yh,d , which follows the Poisson regression model:∗
yh,d ∼ P oisson(yh,d
)
∗
=
yh,d

L


(4)

γh,l · log (Ndl + 1) +6
δh,m · xh,dm ,

(5)

m=1

l=1

where xh,d is a vector of control variables—status variables, word counts, and satisfaction
score. The explanatory variables for the helpfulness regression model have much common
with the variables of the satisfaction probit model, except that the eﬀect of the satisfaction
scores on helpfulness is considered, which is based on the ﬁndings in the literature (e.g.
Ho-Dac et al., 2013; Mauri and Minazzi, 2013; Ludwig et al., 2013). These studies showedthat positive and negative online customer reviews aﬀect readers’ purchase intentions andexpectations. We also explore the eﬀect of the valence of customer reviews on readers’
helpfulness which respect to the review.
Therefore, the full joint likelihood of SL-LDA model is as follows:p(W, Ys , Yh , Xs , Xh , Z, θ, φ, τ, α, β, Λ, γs , γh , δs , δh )
=

D

d=1
D

d=1

p(ys,d | zd , xs,d , γs , δs , τ )p(yh,d | zd , xh,d , γh , δh )Nd
p(wdn | zdn , φ)p(zdn | θd )n=1

p(θd | α)Kp(φk | Λ(k) , β) p(γs , γh , δs , δh ),

(6)

k=1

where p(γs , γh , δs , δh ) is the prior distribution for the regression coeﬃcients, and the settingsof the distribution and hyper parameters are provided in the Appendix.

14

4.2

Model Estimation

In this section, we derive the estimation procedure of the SL-LDA model from the joint
distribution (6). First, we apply the collapsed Gibbs method (Griﬃths and Steyvers, 2004)
for sampling the topic assignment variable z by integrating out topic distribution θ and word
distribution φ. The conditional probability density of topic assignment zdn = k is given as:∗, yh,d , xs,d , xh,d , Z\dn , α, β ∗ , γs , γh , δs , δh , τ )p(zdn = k | wdn = vk , W\dn , ys,d∗Nkvk \dn + βkv∗k∝ Ndk\dn + α Vkp(ys,d| zdn = k, xs,d , γs , δs , τ )p(yh,d | zdn = k, xh,d , γh , δh ),∗v=1 Nkv\dn + βkv(7)where Nkv represents the counts of word v that are assigned to topic k, and the symbol \ rep∗resents the exclusion of the word from the counts. p(ys,d| ·) and p(yh,d | ·) are the probability

density function of the normal distribution and the Poisson distribution, respectively.
Next, for the ordered probit model of satisfaction scores, we apply Gibbs sampling withdata augmentation (Albert and Chib, 1993). Using the results from the literature, the conditional densities of the regression coeﬃcients γs and δs , the augmented continuous satisfaction∗yh,d, and the threshold parameters τ are multivariate normal, truncated normal, and uni-

form distributions, respectively. The details of the posterior density and Markov chain Monte
Carlo (MCMC) procedure are provided in the Appendix.
Finally, we employ the random walk Metropolis-Hastings algorithm to estimate thehelpfulness-Poisson regression parameters—the regression coeﬃcients γh and δh . The joint
conditional density of γh and δh is given by the product of the Poisson density for Yh andthe normal density for the prior distribution. Because the constant term of this posterior
density is unknown and obtaining samples from the posterior is not easy, we employ theMetropolis-Hastings algorithm for sampling from the posterior. The proposal density is anormal distribution with a mean vector of previous samples.
Therefore, the MCMC process by Gibbs and Metropolis-Hastings samplings is as follows.
15

First, we initialize all of the parameter values and then execute the sampling from theposterior according to the previously described densities. After repeated sampling until allof the parameters converge, we calculate the estimates and the highest posterior density
for each parameter using the samples after the burn-in period. Additionally, to estimate
the expectation of the parameters integrated out in (7), θ and φ, the results of the topic
Nkv +β ∗kvk+αassignment are used: θdk = NNddk, φkv =  Nkv +β
∗ .
+α·K
v

5

Empirical Analysis

5.1

Comparison Results

kv

In this section, we compare the proposed model with the extant models to clarify the performance of the proposed model. The diﬀerences between these models are compared from two
viewpoints. The ﬁrst is model selection through the information criterion, and the second
is the interpretability of the extracted topics. Through these two comparisons, we examine
how well the proposed model can improve the interpretability of the topics against the existing models while improving the ﬁt and prediction of the data. The Appendix contains thedetails of the estimation settings.
We consider two comparison models—the separate model and the supervised LDA model.
In the separate model, two processes—extraction of product attributes from the text using
the LDA model and the regression model for satisfaction and helpfulness using the Poisson
regression and the ordered probit model—are separately conducted as divided another models. The supervised LDA model has no constraints related to the labeled words; that is, themodel is equivalent to the proposed model that treats all words as non-labeled. For thesemodels, the parameters are estimated using the Gibbs and Metropolis-Hastings sampling
and the same setting as in the proposed model, as described in the previous section.
First, we discuss the results of the model comparison using the information criteria. To
measure the model comparison, we use the DIC (Spiegelhalter et al., 2002) and the WAIC
16

(Watanabe, 2010). The two criteria compare models from diﬀerent perspectives: the DIC
considers the model’s goodness of ﬁt and complexity, and the WAIC assesses the model’s
generalization error. Figure 2 illustrates the values of the DIC and WAIC as the summationof those for two dependent variables of product satisfaction and readers helpfulness in therange of the number of topics from 5 to 15 for three models. The comparison of the separate
and supervised models show that the separate model is inferior to the others for all numbersof topics and both criteria. Whereas both the separate and supervised models have beenused in previous studies (e.g., Moon and Kamakura, 2017; Büschken and Allenby, 2016,
respectively), the supervised model is superior in terms of model comparison based on theinformation criterion.
A comparison of the proposed model with the supervised LDA model shows that thesupervised LDA model is better overall, even though the proposed model has smaller DIC
and WAIC for certain numbers of topics. One possible explanation for this ﬁndings is thatthe proposed model has restrictions of labeled words, and the parameters with respect tothese words are ﬁxed in certain values and might deviate from values that achieve a better
ﬁt, whereas the supervised LDA model does not have such restrictions. Interestingly, thediﬀerence between both models is not large, especially when compared with the WAIC, forwhich both models are almost the same. This ﬁndings indicates that the impact of thelabeled word constraints on generalization is relatively small, and the proposed model isworthwhile if the beneﬁts from the improvements in topic interpretability by labeling seedwords are greater than the impact of the restriction.
Next, we compare the proposed model with the supervised LDA model from the perspective of topic interpretability. Tables 2 and 3 provide the top 15 words distributed for each
topic, in descending order. For a simple comparison, the number of topics is ﬁve for bothmodels, which is the same as the number of expected perceived topics determined before
the analysis. Table 2 shows that, for the proposed model, ﬁve interpretable topics extracted
are attributable to the labeled words. In addition, the proposed model can assign seemingly

17

DIC

WAIC

11000

10000

Model

9000

Separate
S_LDA
SL_LDA

8000

7000

6000
5

6

7

8

9

10

11

12

13

14

15

5

6

7

8

9

10

11

12

13

14

15

Number of topics

Figure 2: Values of DIC (left) and WAIC (right): Lines indicate comparison models, separate
model (red), supervised LDA model (green), and proposed model (blue). Dots represent thesmallest values among the variations in the number of topics for each model.

correct topics to words that are related to the labeled words and, hence, are appropriate to
make up the same topic. For example, lime and chili are in the ﬂavor and taste topic, size
is in the packing topic, and diet is in the healthy topic; these words are non-labeled but
appropriate for constituting the same topic along with the labeled words. In contrast, most
topics in Table 3 for the supervised LDA model consist of meaningless sets of words, andthe same words are related to multiple topics. For example, diﬀerent topic words, such as
weight, price, and calorie, are in the same topic 3, and the same words, such as bag andﬂavor, are related to multiple topics. Therefore, humans face diﬃculties interpreting themeanings of topics using the supervised LDA model.
In summary, these results show that the proposed model is reliable in both explaining
the variation in product satisfaction and readers’ helpfulness and interpretability of topics
by labeling certain seed words as representative words for the topics. In the next section, we
discuss the estimation results of the regression parameters.

18

Table 2: Top 15 words in descending order of word distribution for each topic in supervisedlabeled LDA model (asterisks next to words indicate labeled words)
Flavor & Taste Packing
Healthy
ﬂavor*
bag*
calorie*taste*
box*
fat*
salt*
case*
healthy*sweet*
product*
fry
bbq*
pack*
delicious
vinegar*store*
alwayssalty*
weight*crave
pepper*size
pertexture*
order
diet
lime
whole
star
crunchneed
evercrunchy*
watcher recommendchilivarietysodiumcheddar*keep
lunch
tasty*
addictlook

Money & Buying Ingredienttry*
ingredient*
get*
oil*
buy*
rice
ﬁnd*
ﬂour*
bought*deﬁnitely
price*
corn
order
kid
originback
know
thought
since
ﬁnal
bad
disappoint
healthierkindfarpurchasegreasysay
addictlist

Table 3: Top 15 words in descending order of word distribution for each topic in supervised
LDA model
Topic 1
Topic 2 Topic 3
sweet
bag
bag
fat
taste
ﬁnd
taste
try
weightcalorie
buy
buy
ﬂavor
get
price
rice
package calorie
healthy
box
store
bag
order
healthyingredient
pack
get
salty
boughtbbq
oilpurchase
size
bad
never
order
ﬂour
look
every
fry
said
day
sodiumpeoplekeep
19

Topic 4
ﬂavor
taste
try
lime
chili
textureexpectthoughget
spicy
product
need
salt
caloriesay

Topic 5ﬂavor
salt
vinegar
taste
try
bbq
pepperorigincaloriegarliccheddarorder
prefer
fat
sour

5.2

Discussion of Estimation Results

Table 4 shows the estimation results of the proposed model when the number of total topics
is 14, which is the smallest value of both criteria in Figure 2. Table 4 provides that theestimated posterior mean of the thresholds parameters and the regression coeﬃcients oflabeled topics and control variables. The asterisks next to the posterior mean indicate theparameter signiﬁcance calculated by the 95% highest posterior density.
First, the threshold parameters (τr ) indicate that an approximate 0.50 increase in thelatent continuous rating is associated with a one-point increase in the observed discrete rating. Because the explanatory variables for the topics represent the log-transformed numberof words assigned to the topic, the regression coeﬃcients on the labeled topics (γl ) can beinterpreted as substantial changes in the ratings for a 1% increase in the number of words
for that topic. For example, if the number of words associated with the topic “ﬂavor andtaste” increases by 1%, the expected change in the latent rating is −0.58, translating to analmost one-point decline in customer satisfaction.
Table 4 also provides some interesting ﬁndings related to the regression coeﬃcients. The
coeﬃcients of topics 1, 2, and 5 for the satisfaction score are negatively estimated, and only
the coeﬃcients of topic 3 are positively estimated. This ﬁnding indicates that dissatisﬁed
customers are more likely to talk about “ﬂavor and taste,” “packing,” and “ingredient”
topics, and satisﬁed customers are more likely to talk about the “health” topic. Additionally,signiﬁcant coeﬃcients of the helpfulness regression are related to the health and ingredient
topics. This result suggests that reviews including “health” topic words are regarded as
helpful, and reviews including “ingredients” topic words are less helpful.
Remarkable ﬁndings are also found in the coeﬃcients for the control variables (δm ). Because the coeﬃcients for the purchase veriﬁcation are positively signiﬁcant for both objective
variables, customers who purchased the product can be viewed as being satisﬁed relative tocustomers who do not make a purchase, and readers ﬁnd that reviews by customers who
made purchase are more helpful. Similarly, more satisﬁed customers write longer reviews,
20

Table 4: Estimation results of proposed modelParameters
τ1
τ2
τ3
τ4
γ1 (Flavor & Taste topic)
γ2 (Packing topic)
γ3 (Health topic)
γ4 (Money & Buying topic)
γ5 (Ingredient topic)
δ1 (Purchase veriﬁcation)
δ2 (Top contributor)
δ3 (Top reviewer)δ4 (Vine voice)δ5 (Word counts)
δ6 (Satisfaction score)

Posterior Mean (95 % HPD interval)
Satisfaction Score
Helpfulness Count−2.095* (−2.340, −1.843)—−1.635* (−1.808, −1.425)—−1.102* (−1.272, −0.937)—−0.587* (−0.761, −0.392)—−0.584* (−0.773, −0.404)
0.081 (−0.018, 0.182)−0.382* (−0.605, −0.160)
0.009 (−0.123, 0.139)
1.046* (0.797, 1.412)
0.238* (0.050, 0.439)
0.075 (−0.190, 0.338)−0.114 (−0.274, 0.044)−1.612* (−2.147, −1.115) −0.274* (−0.535, −0.019)
0.294* (0.073, 0.502)
0.234* (0.059, 0.410)
0.678 (−0.852, 2.161)
1.004* (0.234, 1.773)
0.748 (−1.494, 3.128)−0.489 (−1.682, 0.707)
0.593 (−0.924, 2.043)
0.076 (−0.742, 0.931)
0.012* (0.006, 0.018)
0.004* (0.001, 0.007)—−0.152* (−0.190, −0.113)

and such reviews are regarded as helpful for readers. The last ﬁnding is the negative eﬀectof the satisfaction score on readers’ helpfulness. Readers tend to ﬁnd critical reviews withlow satisfaction scores more helpful than positive and satisﬁed reviews.
In conclusion, these results answer our research questions: “What is the relationshipbetween product attributes and the satisfaction of customers who make purchases?” and“What attributes do readers of the customer review expect to ﬁnd as useful information?”
Additionally, these ﬁndings are obtained by labeling seed words and extracting interpretable
perceived topics, and paying attention to interpretability when using topic models in customer review analysis may be necessary.

21

6Conclusion

We introduced a supervised labeled latent Dirichlet allocation (SL-LDA) model that combines word labeling to extract interpretable perceived topics with the supervised LDA to
explore the structure of the satisfaction of experienced customers and the expectation of review readers. To obtain stably interpretable latent topics in online customer reviews, priori
labeled words related to product attributes are assigned respective topics as semi-supervised
learning. Accordingly, we connect product attributes with customer satisfaction as feedback
from past customers and consumer interests for products as perceived helpfulness of future
customers through supervised learning.
The model comparison in Section 5.1 shows that the proposed model is not only reliable
in explaining the variations in customer satisfaction and readers’ helpfulness but also caninterpret topics relative to the extant model. When comparing the proposed model with thesupervised LDA model, the diﬀerence between both models is not large in terms of model
selection through the information criterion. Furthermore, a comparison of the word sets ofextracted topics shows that the words obtained in the proposed model are easy to interpretbecause they consist of labeled words and other words assigned to the topic according to thelabeled words. In contrast, because the supervised LDA model has no such restriction, we
may obtain a set of words whose cohesion is incomprehensible to humans. Therefore, theproposed model is worthwhile if the beneﬁts from improving the topic interpretability using
word labeling are greater than the impact of the restriction.
In this empirical study, we ﬁnd that the “ﬂavor and taste,” “packing,” and “ingredient”
topics are mentioned by dissatisﬁed customers, and reviews including the “ingredient” topic
are likely recognized as unhelpful by readers of the reviews. In contrast, “health” topic
has a positive eﬀect on both customer satisfaction and perceived helpfulness. Companies
and marketers can use the proposed model to understand the relationship between productattributes and the satisfaction of customers making purchases and the attributes that readersof customer reviews expect to ﬁnd as useful information.
22

Future research is needed in two aspects related to marketing improvement. One aspect isimproving the product attribute analysis through a consideration of consumer heterogeneity(Xiao et al., 2016) and word sentiments (Decker and Trusov, 2010; Archak et al., 2011) todevelop a more sophisticated model. Another aspect is using the topic model to predict
more “supervised” objectives such as detecting fake or deceptive reviews (Qi et al., 2016),
which are meaningless reviews and high score ratings after false purchase behavior.

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25

Appendix
A

Posterior Distributions and Estimation Procedure

In this section, we describe the details of posterior distributions for the proposed model andthe MCMC algorithm. Equations for sampling from posterior distributions of the satisfaction
regression parameters are as follows:p(γs | Ys∗ , Z, Xs , δs , gs,0 ) ∼ N (μγs , Σγs ),Σγ s =

D


−1

−1
log(Nd· + 1) log(Nd· + 1) + gs,0·I

d=1μγ s = Σ γ s

D


5


∗
log(Nd· + 1) ys,d−,δs,m xs,dm

(8)

m=1

d=1

p(δs | Ys∗ , Z, Xs , γs , ds,0 ) ∼ N (μδs , Σδs ),
−1
D

−1
Σδ s =
xs,d x,s,d + ds,0 · I
d=1μδ s = Σ δ s

D

d=1

xs,d

∗
−
ys,d

L



γs,l log(Ndl + 1)

(9)

l=1

∗
p(ys,d| ys,d , zd , xd , γs , δs , τ ) ∼ NLγs,l log(Ndl + 1) +l=1

truncated to (τr−1 , τr ] if ys,d = r∗∗
p(τr | Ys , Ys∗ , τq ) ∼ U [τlhs, τrhs],5δs,m xs,dm , 1 ,

m=1

(10)

r = 1, . . . , R − 1, q = r∗∗
= max max{ys,d; ys,d = r}, τr−1
τlhs
∗
∗
τrhs
= min min{ys,d; ys,d = r + 1}, τr(11)

The parameters for helpfulness regression are sampled Metropolis-Hastings method, andthe proposal density is the normal distribution with mean of own values in the previous
(t)

(t−1)

iteration, γh ∼ N (γh, σγ2h · I). σγ2h is a step size parameter whose value is adjusted in the

MCMC procedure so that the acceptance rate falls into the range from 30% to 50%. Because
26

(t)

the proportion of proposal densities can be canceled, the acceptance ratio in sampling γhconsists of the proportion of posterior distributions:(t)αγh = min 1,(t)p(Yh | Z, Xh , γh , δh )p(γh | gh,0 )(t−1)

p(Yh | Z, Xh , γh(t)p(Yh | Z, Xh , γh , δh ) =

D


(t−1)

, δh )p(γhP o(yh,d ;

d=1

L


| gh,0 ),(t)γh,l log(Ndl + 1) +

l=1

6


δh,m xh,dm )

m=1

(t)
(t)
−1
· I)
p(γh | gh,0 ) = N (γh ; 0, gh,0(12)

δh is also sampled in the same way.
Therefore, the algorithm of MCMC procedure is as follows:
1. initialize Z, γs , δs , Ys∗ , τ, γh , δh , σγh , σδh
2. iterate sampling until all parameters converged(a) sample Z according to equation (7)(b) sample γs , δs , Ys∗ , τ according to equations (8) to (11)(c) update γh and δh with the acceptance ratio (12)(d) adjust σγh and σδh if the cumulative number of the acceptance falls outside thedesired percentage
3. calculate the expectations of θ and φ using the last samples of Z
In Section 5, we repeat the above MCMC process 50,000 times, and then we use 25,000
samples excluding the burn-in samples to calculate the posterior means and the intervals of27highest posterior density. The settings of prior distribution used in Section 5 are as follows:θd ∼ Dirichlet(α),αk = 1.0 ∀k

φk ∼ Dirichlet(Λ(k) β),βv = 1.0 ∀v−1γs ∼ N (0, gs,0
· I),

gs,0 = 0.1

δs ∼ N (0, d−1s,0 · I),ds,0 = 0.1

−1
· I),
γh ∼ N (0, gh,0

gh,0 = 0.1

δh ∼ N (0, d−1h,0 · I),dh,0 = 0.1

28