Content uploaded by Pradnya Bhagat
Author content
All content in this area was uploaded by Pradnya Bhagat on Jan 24, 2019
Content may be subject to copyright.
A Comparative Study of Feature Extraction Methods from User
Reviews for Recommender Systems
Pradnya Bhagat
Department of Computer Science and Technology, Goa
University
Goa, India
pradnyabhagat91@gmail.com
Jyoti D. Pawar
Department of Computer Science and Technology, Goa
University
Goa, India
jdp@unigoa.in
ABSTRACT
The Recommender systems technology is being massively exploited
by e-commerce giants to enhance the shopping experience of their
clients which in turn helps in improving the sales of the company.
Most of the recommender systems in use today are based on Col-
laborative Filtering (CF) in which the known preferences of a group
of users are used to make recommendations or predictions for the
unknown preferences of other users. Although these ratings com-
municate about the quality of the product, they almost most of the
times fail to express the reason behind people believing the product
to be of a particular quality. This information can be inferred if
analyze the information rich textual reviews written by the users.
In the current work, an attempt is made to study and implement
various methods described in literature, to mine the product features
from the user reviews associated with the product. A comparative
study is presented at the end to appreciate the performance of the
methods.
CCS CONCEPTS
•Information systems →Recommender systems
;
•Comput-
ing methodologies →Natural language processing;
KEYWORDS
Recommender Systems, Collaborative Filtering, User Reviews, Prod-
uct Features, POS Tagging, Apriori, Latent Dirichlet Allocation
ACM Reference Format:
Pradnya Bhagat and Jyoti D. Pawar. 2018. A Comparative Study of Feature
Extraction Methods from User Reviews for Recommender Systems. In CoDS-
COMAD ’18: The ACM India Joint International Conference on Data Science
& Management of Data, January 11–13, 2018, Goa, India. ACM, New York,
NY, USA, 4 pages. https://doi.org/10.1145/3152494.3167982
1 INTRODUCTION
The 1990s decade saw the evolution of web as a social networking
platform where people could communicate with each other and
express their opinions publicly on a global scale. Businesses and
individuals started taking advantage from this technology by being
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for prot or commercial advantage and that copies bear this notice and the full citation
on the rst page. Copyrights for components of this work owned by others than ACM
must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specic permission and/or a
fee. Request permissions from permissions@acm.org.
CoDS-COMAD ’18, January 11–13, 2018, Goa, India
©2018 Association for Computing Machinery.
ACM ISBN 978-1-4503-6341-9/18/01. . . $15.00
https://doi.org/10.1145/3152494.3167982
able to connect to potential customers throughout the world. This
has led to the emergence of e-commerce websites that have provided
a platform to thousands of vendors to expand their business across
the globe.
Although having many advantages, it is still not possible to get
tangible experience for the products available on these websites.
It may become a challenging task to validate the description and
quality of the features through the descriptions made available by
the vendor of the product. As a result, e-commerce websites have
made it possible for the customers to share their experiences about
the products with other customers to help them in making wise
decisions. CF has proved to be the most successful technology in the
development of recommender systems till date. Most of the research
work on CF focuses on the explicit ratings specied by the users
(Ex: 1- 5 stars), or implicit indications (purchases or click-throughs).
Though these ratings indicate the quality of the product, they fail to
quantify the reason for the product achieving that particular quality.
In other words, there is no information available, presenting the
dierent features of the product and the quality of these features.
To overcome these limitations, a better methodology can be
developed if we look beyond the star ratings of the products and
take into consideration the textual reviews written against every
star rating by the customers. The reviews written by the customers
are rich sources of information about the features of a product and
their quality as perceived by the users.
In our work, an attempt is made to study the reviews written
by the customers on an e-commerce website. With respect to this
work, our scope is limited to study and implementation of dierent
methods to automatically extract product features from the text
reviews.
2 LITERATURE SURVEY
A signicant amount of work has been done in recent years to
extract product features from the textual reviews. [
4
] presents an
approach to extract single noun and bi-gram features from user
reviews using a combination of Natural Language Processing (NLP)
and statistical methods. The approach assumes that the bigram
topics can either be made up of Noun-Noun (NN) pairs or Adjective-
Noun (AN) pairs. [
1
] presents an Apriori algorithm to nd frequent
itemsets from a transaction dataset. The approach has been adopted
greatly in literature[
2
] to discover important topics (features) from
text documents. The approach make a further assumption that if
some words repeatedly occur together or in close proximity to one
another in review sentences, that means they together form some
important feature about that product. Hence, the algorithm is able
to nd multiword features without imposing any limitation on the
CoDS-COMAD ’18, January 11–13, 2018, Goa, India P. Bhagat et al.
number of words permitted in a feature. [
3
] uses an unsupervised
technique of Latent Dirichlet Allocation (LDA) for topic extraction.
The method is able to extract the main topics and the correspond-
ing important words from the reviews. [
7
] presents a probabilistic
approach for mining user preferences from reviews and mapping
them onto numerical ratings based on Naive Bayes classier. [
11
]
attempts a statistical approach to identify polarity of nouns where
no sentiment word is explicitly associated with the nouns. [
8
] pro-
poses a domain independent approach to predict intensity of the
sentiments expressed.
3 EXPERIMENTAL AND COMPUTATIONAL
DETAILS
The experimental work carried out consists of studying various
methods described in the literature to extract product features
from a corpus of text reviews and making a comparison based on
the features retrieved. The dataset [
5
] used in the experiments is
sourced from Amazon.com which is limited to the category Mobile
Cell Phones and Other Related Accessories.
3.1 Feature Extraction using Noun Occurrence
(FENO)
An analysis of the dataset unveiled that most of the product fea-
tures occur as nouns in the review dataset. Hence, as a preliminary
method we consider all the nouns as the features in the dataset. The
challenge here is, along with the product feature nouns, there are
many other nouns that occur in the product reviews which in no
way represent product features (non-feature nouns). For example, if
we consider the following sentence:
Example 1: My family loved the look of the cellphone.
After Parts of Speech (POS) [9] [10] tagging we get:
My_PRP family_NN loved_VBD the_DT look_NN of_IN the_DT
cellphone_NN.
We can observe that, there are three nouns in the given sentence;
family,look and cellphone. While we want to retain look and cell-
phone in our result set, since they form the features of a particular
item belonging to Mobile phones and accessories category, the noun
family clearly does not constitute as a product feature in the given
dataset. Further observation of the dataset revealed that frequency
of occurrence of feature nouns is considerably more than the fre-
quency of occurrence of non-feature nouns. Hence, we eliminate
the non-feature nouns by retaining only those nouns which appear
more number of times than a specied threshold. As the nal result
set, we extract the top 25 nouns from the generated nouns based
on the occurrence frequency.
3.2 Single Word Feature Extraction Using
Occurrence Patterns (SW-FEOP)
It is observed that most of the feature nouns occur in close proximity
to sentiment words [
4
]. This pattern is not followed by non-feature
nouns. For example, consider a sentence:
Example 2: My friend suggested me to buy this awesome phone
because it has an excellent camera.
After POS tagging, we get:
My_PRPS friend_NN suggested_VBD me_PRP to_TO buy_VB this_DT
awesome_JJ phone_NN because_IN it_PRP has_VBZ an_DT
excellent_JJ camera_NN.
The nouns occurring in the above sentence are; friend,phone
and camera. As can be seen, phone has an adjective awesome as-
sociated with it and camera has an adjective excellent associated
with it. Since the reviewer wants to describe the phone, he will use
some sentiment words to express his opinions about the features
of the product. On the other hand, if words like friend,neighbor or
relative occur also in the review, there will hardly be any sentiment
words associated with them. This information can be utilized to
dierentiate feature nouns from non-feature nouns. Hence, in this
method only the nouns which are associated with sentiment words
in the reviews are extracted. The top 25 most occurring nouns from
the results generated are considered in the nal result set.
3.3 Bi-gram Feature Extraction Using
Occurrence Patterns (B-FEOP)
This method tries to extract bi-word features from the review set
[4]. Consider the following two sentences as example:
Example 3: The camera mode of the mobile is good.
The front camera of the mobile is good.
POS tagging of these sentences gives us:
The_DT camera_NN mode_NN of_IN the_DT mobile_NN is_VBZ
good_JJ.
The_DT front_JJ camera_NN of_IN the_DT mobile_NN is_VBZ
good_JJ.
As can be seen in the rst sentence, camera_NN mode_NN forms
one topic, which is formed by a noun followed by a noun. In the
second sentence, the words front_JJ camera_NN form one topic
which is formed by an adjective and a noun occurring consecutively.
Hence, to produce a set of bi-gram topics, all bi-grams from the
global review set which conform to one of following basic POS
co-location patterns are extracted:
(1) A noun followed by a noun (NN) such as camera mode.
(2)
An adjective followed by a noun (AN) such as front camera.
There are candidate topics that need to be ltered out to avoid
including ANs that are actually opinionated single-noun topics; for
example, excellent camera also forms an adjective-noun pair, but is
a single-noun topic (camera) and not a bi-gram topic. To achieve
this, the bi-grams whose adjective is found to be a sentiment word
(e.g. excellent, good, great, lovely, terrible, horrible etc.) are excluded
using an English opinion lexicon [6].
3.4 Feature Extraction using Frequent Itemset
Generation (FEFIG)
The method attempts to extract topics using Apriori frequent item-
set generation algorithm [1]. The algorithm works in two steps:
(1)
In the rst step, it nds all frequent itemsets from the trans-
actions that satisfy a user specied minimum support.
(2)
In the second step, it generates rules from the discovered
frequent itemsets.
To generate topics from the reviews, we break down the reviews
into sentences and consider every sentence as one transaction. Next,
A Comparative Study of Feature Extraction Methods from User Reviews for Recommender SystemsCoDS-COMAD ’18, January 11–13, 2018, Goa, India
after applying the pre-processing steps, we keep only the nouns
and the adjectives from every sentence, since nouns and adjectives
are observed to be the terms representing features of the product.
The assumption is such that, the features from all the nouns and
adjectives will be the most frequently occurring terms (items) and
hence will have higher support. So, to nd the frequently occurring
terms, we need only the rst step of the Apriori algorithm, i.e. to
nd the frequent itemsets which are candidate features. The results
of applying the Apriori algorithms on our dataset is given below.
The support threshold applied is 2% while generating the results
and the top 25 item sets are extracted as the nal result set.
3.5 Feature Extraction using Latent Dirichlet
Allocation (FELDA)
In this method the statistical model Latent Dirichlet Allocation [
3
]
is used in which a document is considered to have a set of topics.
The model represents documents as mixtures of topics that are
made up of words with certain probabilities.
As it is seen in the above methods, adjectives and nouns are
the only parts of speech that constitute to product features in the
dataset. Hence, to get better results, only the adjectives and nouns
from reviews are retained and LDA is applied. Since in our dataset
we already know the topic and are only interested in nding im-
portant words constituting that topic, we set the number of topics
parameter to one.
4 RESULTS AND DISCUSSIONS
Table 1 displays the results of experimented methods on the stated
dataset[
5
]. Only the most frequently occurring top 25 features
obtained using the experimented methods are considered for the
comparative study.
Manual evaluation is be used to evaluate the success rate of the
methods. The features obtained using the experimented methods
are reviewed manually and are divided into two categories; fea-
tures that constitute to product features and features obtained that
do not contribute to the product features in the Cell Phones and
Accessories category. The success rate of the methods is explained
with the help of a graph in Figure 1.
Figure 1: Performance of methods experimented
Table 1: Features obtained using experimented methods
FENO SW-FEOP B-FEOP FEFIG FELDA
1 battery battery battery life battery battery
2 button bit battery pack button cable
3 cable case belt clip cable car
4 car charge car charger case case
5 case charger cell phone case
phone charge
6 charge cover customer service charger charger
7 charger deal external battery device device
8 color design few days easy easy
9 cover device galaxy note good good
10 day feature galaxy s3 great great
11 device t galaxy s4 iphone iphone
12 headset job home button little little
13 iphone part iphone 4s nice nice
14 phone phone new trent other phone
15 port plastic only thing phone port
16 power price phone case power power
17 price product power bank price price
18 problem protector power button product product
19 product quality same time protector protector
20 protector review samsung galaxy quality quality
21 quality screen screen protector screen review
22 review side sound quality screen
protector screen
23 screen thing usb cable thing speaker
24 thing time usb port time time
25 time way wall charger usb usb
5 CONCLUSION
The work compares performance of ve feature extraction methods
on a real world dataset [
5
]. As can be concluded, FENO being the
simplest detects only single word features. Being a basic method,
it does not guarantee high quality results. The second method
SW-FEOP, tries to improve upon the rst method by adding an
additional constraint of a sentiment word being associated with
the noun. As can be seen from Figure 1, this method delivers better
performance compared to all other methods. The third method
B-FEOP tries to nd multi-word features by considering NN and
AN pairs. It identies that a product feature is mostly preceded
by an adjective which may or may not be a sentiment word. FE-
FIG is based on Apriori algorithm and is able to nd multiword
features without any length limitation. Since Apriori algorithm
makes multiple passes on the data to nd the itemsets, this method
is considerably slower than all other methods. FELDA tries to nd
topics using LDA. Since it considers every word as an independent
entity, we get only single word topics using this method.
The future work plan consists of improving upon the existing
feature extraction methods. The sentiments corresponding to fea-
tures and the intensity of the sentiments associated also needs to
be studied. Based on this, we propose to improve upon the exist-
ing recommender system algorithms by adding the dimension of
context to the recommendation algorithms.
CoDS-COMAD ’18, January 11–13, 2018, Goa, India P. Bhagat et al.
REFERENCES
[1]
Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast Algorithms for Mining
Association Rules. Proceedings of the 20th VLDB Conference Santiago, Chile (1994).
[2]
Ruihai Dong, Kevin McCarthy, Michael P. O’Mahony, Markus Schaal, and Barry
Smyth. 2012. Towards an Intelligent Reviewer’s Assistant: Recommending Topics
to Help Users to Write Better Product Reviews. IUI’12, Lisbon, Portugal (2012).
[3]
Ruihai Dong, Markus Schaal, Kevin McCarthy Michael P. O’Mahony, and Barry
Smyth. 2012. Unsupervised Topic Extraction for the Reviewer’s Assistant.
Springer-Verlag London (2012).
[4]
Ruihai Dong, Markus Schaal, Michael P. O’Mahony, and Barry Smyth. 2013. Topic
Extraction from Online Reviews for Classication and Recommendation. Proceed-
ings of the Twenty-Third International Joint Conference on Articial Intelligence
(2013).
[5]
Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual
evolution of fashion trends with oneclass collaborative ltering. W WW (2016).
[6]
Minqing Hu and Bing Liu. 2004. Mining and Summarizing Customer Reviews.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining (KDD-2004) (2004).
[7]
Cane Wing ki Leung, Stephen Chi-fai, Chan Fu-lai, and Chung Grace Ngai. 2011.
A probabilistic rating inference framework for mining user preferences from
reviews. World Wide Web (2011).
[8]
Raksha Sharma, Mohit Gupta, Astha Agarwal, and Pushpak Bhattacharyya. 2015.
Adjective Intensity and Sentiment Analysis. Proceedings of the 2015 Conference
on Empirical Methods in Natural Language Processing (2015).
[9]
Ann Taylor, Mitchell Marcus, and Beatrice Santorini. 2003. The Penn Treebank:
An Overview. In: Abeille A. (eds) Treebanks. Text, Speech and Language Technology,
vol 20. Springer, Dordrecht (2003).
[10]
Kristina Toutanova, Dan Klein, Christopher Manning, , and Yoram Singer. 2003.
Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In
Proceedings of HLTNAACL (2003).
[11]
Lei Zhang and Bing Liu. 2011. Identifying Noun Product Features that Imply Opin-
ions. Proceedings of the 49th Annual Meeting of the Association for Computational
Linguistics:shortpapers (2011).
