Opinion mining on Digital Camera Reviews: A Natural
Language Processing Approach
Mr. Amey D S Kerkar
Ms. Manisha Fal Dessai
Assistant Professor,
Don Bosco College of Engineering,
Fatorda-Goa, India.
Assistant Professor,
Don Bosco College of Engineering,
Fatorda-Goa, India.
amecop47@gmail.com
manishagfd@gmail.com
ABSTRACT
Our paper deals with the technique of extracting the opinions
about a product which are written in the form of reviews by
the product users on the various E-Commerce and the product
review sites and then summarize these opinions to present a
one page summary which ultimately acts as a
recommendation system for a willing-to-buy customer. We
have implemented a system that extracts the reviews from
different review sites and then, from each review it tries to
find out the opinions and the features on which the opinions
are made. For the implementation we have considered reviews
for Sony Cyber shot Digital Camera. Then using natural
language processing (NLP) techniques we have designed and
implemented opinion orientation (OO) algorithm that decides
positivity or negativity for an opinion and finally produces
one page graphical summary and recommendation about the
product. The paper also presents various observations that we
recorded after the implementation.
Keywords
Opinion mining, opinion orientation, feature, opinion, review,
Part-of-Speech, Natural Language Processing
1. INTRODUCTION
Today E-Commerce has become a way of life and website
owners that satisfy the requirements of the online shopper are
fattening their wallets. People prefer buying goods online
rather than having to travel from store to store. online
shoppers simply navigate from one web page to the next
comparing the stores and the wares of those stores. With
online shopping they can review and compare dozens of
stores and products at once. Just before deciding upon which
brand or which model of a product to buy, the potential
customer likes to review the product with respect to its
performance, quality and price. The customer then seeks for
the opinions from those who have been already using this
product. Almost all the well-known E-commerce sites have
the provision for the customers to share their experience by
writing reviews on the products they have purchased once
they start using the product. As the popularity of product
grows, the number of reviews that the product receives also
grows rapidly.
The reviews are written using a natural language
like English which has enormous amount of vocabulary of
words which is still growing. On Every day basis new words
are being added to this list. A single word might have a
number of synonyms Ex. “Murder” and “Kill” means the
same, a sentence sometimes can be written in terms of
acronym. Ex. “As soon as possible” can be written as “ASAP”
or” asap”. English language has lot of grammatical rules
based on Part-Of-Speech (POS) elements. So in order to
extract opinions from these sentences the opinion mining
system has to first understand the sentence. Identify whether
the sentence is a factual sentence or an opinion sentence. A
popular product might have a huge number of reviews written
on a single review site. . In a recent observation made we have
found that Canon EOS 600D DSLR digital cameras have got
over 170 reviews on flipkart.com alone and 219 reviews on
amazon.in. Also, it is seen that reviews are sometimes very
long which makes it hard for a potential customer to read each
and every sentence and then make a decision. In Natural
Language form of writing we cannot keep any restrictions on
the reviewer and hence out of say 120 sentences written by
him hardly say 5 or 6 sentences might be opinionated
sentences. On the other hand, if the willing-to-buy customer
reads only the first few reviews or first few lines of a review
then he or she gets a biased view. Many a times a review is
lengthy, which makes it hard for a potential customer to read
it completely and make decision on whether to purchase the
product or not. If the potential customer reads only a few
reviews, he gets a biased view. The large number of reviews
also makes it hard for product manufacturers or businesses to
keep track of customer opinions and sentiments on their
products and services. It is thus highly desirable to produce a
summary of reviews [3].
1.1.Existing System
Textual information processing has been focused on mining
and retrieval of factual information, e.g. information retrieval,
Web search, text classification, text clustering and many other
text mining tasks. Opinion mining Opinion mining topic is
explored and researched upon by many researchers
worldwide. Our work is related to mining opinion features in
customer reviews (Minqing Hu and Bing Liu 2004).where
much of the work is done using association rule mining a
concept in data mining. In our paper we have tried to mine the
opinions using Natural Language Processing technique and
list the consequences involved in using this technique.
1.2.Proposed System
Our proposed system uses a corpus of text documents, where
each document is a review written by each user. We have
collected over 500 reviews. These reviews are regarding Sony
Cyber Shot Camera and are collected manually from various
reliable review sources like amazon.com, epinion.com,
Google.com, gsmarena.com, rediff.com.
There is a known feature list consisting of all the features
any willing-to-buy user will like to stress upon before
purchasing the digital camera. There is also an opinion
lexicon which is generated during the execution which
determines an opinion word and its corresponding orientation
i.e. positive, negative or neutral. By using the opinionated
feature list and Opinion Lexicon on the corpus, the proposed
system does five important activities [4]:
1. Identifies the opinionated sentences from the user
generated content.
2.
For each such sentence, finds out the features on
which the opinions are expressed.
3.
For each feature finds out the opinion orientation
i.e. positive or negative or neutral (in case of
context dependent words).
4.
Summarization of opinions on the most relevant
features (i.e. most opinionated features).
The summary thus generated is textual as well as graphical
which can be now presented to the user along with the
recommendation page which helps him/her to make a
decision.
feature mapping is required so that all the synonyms converge
down to one feature. Features can be identified in two ways:
2.2.1.
Natural Language processing can be used here where we
have to determine part-of-speech tags also called POS
tagging [4]. First, we have to find the frequent nouns and
noun phrases. Nouns and noun phrases (or groups) are
identified by using a POS tagger. Their occurrence
frequencies are counted, and only the frequent ones are
kept. A frequency threshold can be determined. The
reason for using this approach is that when people
comment on product features, the vocabulary that they use
usually converges, and most product features are nouns.
Thus, those nouns that are frequently talked about are
usually genuine and important features. Those nouns that
are infrequent are likely to be non-features or less
important features [2].
2.2.2.
2. OPINION MINING TECHNIQUE
2.1. PRE-PROCESSING
The text corpus collected from different sites has to be preprocessed before being used for opinion mining purpose. Preprocessing involves:
2.1.1.
Tokenization
Tokenization is the process of breaking a stream of text up
into words, phrases, symbols, or other meaningful elements
called tokens. All contiguous strings of alphabetic characters
are part of one token; likewise with numbers. Tokens are
separated by whitespace characters, such as a space or line
break, or by punctuation characters. Punctuation and
whitespace may or may not be included in the resulting list of
tokens.
2.1.2.
Stop-word removal
Stop-words are the frequently occurring unimportant words,
removing which does not have much effect on the information
contained in the document but its removal only helps to
reduce the size of the corpus and thus leads to faster
processing.
2.2.Feature Extraction
Given a text review document, this step identifies features
of the product on which the customer have expressed their
opinions on. The features of the reviewed product must be
known in advance [4]. The main difficulty is to determine the
features. There might be hundreds of features and sub-features
in a product. For example, if the product is a car then there are
more than 50 features on which users can comment on. All the
features are not important but only some are more important.
If a person wants to buy a car he would be rather interested in
only the opinions on key features of the car rather than other
common and unimportant features like seat covers etc.
Second difficulty is that each feature will have n number of
synonyms. Hence, if in one sentence user says “display is
bright and powerful” and in other sentence “screen is bright
and powerful”, then the system should identify that both these
sentences are actually opinions on the same word called
display which is sometimes also called as screen. Thus,
Part of Speech Tagging (dynamic
feature extraction)
Known Feature List [2]
The advantage of using this approach is that it solves
domain related issues as the feature list is manually
constructed. If we know the product domain, then it is
easy to determine what features the user is generally
interested in are. For example, if the product is a digital
camera then we know in advance the features which are to
be reviewed for any digital camera and also the commonly
used synonyms for each feature.
Table 1 . Sample Feature List with Synonyms for some
features of a Digital Camera
Feature
Synonyms
Battery
charge, powerup, charger, power
Picture
Images, shots, photos, pics, shots
Screen
display, lcd
Resolution
pixel, firmness, closure
Memory
storage, capacity, store, retention
Price
cost, value, rate, amount
Body
look, feel, design, pattern, figure
This feature list is then compared with the sentences in each
document of the corpus. Feature mapping is done where each
synonym of a feature is replaced by the feature word. Thus
features from the document are determined.
2.3.Opinion Identification and
Orientation
We use the Lexicon based approach by first extracting the
adjectives and adverbs from the POS tagged Corpus. These
words are extracted in three files by comparing with a
generalized list of positive and negative opinion words where
the first file stores all positive words, the second stores all
negative ones and the third stores context dependent or neutral
words. Then the opinion list is generated with the opinion
word and its initial orientation assigned i.e. positive, negative
or neutral. Examples of positive opinion words are: beautiful,
wonderful, good, and amazing. Examples of negative opinion
words are: bad, poor, and terrible. Examples of neutral
opinion words are: large, long and small.
2.3.1.
Feature Orientation Identification
Given a sentence that contains a feature, this step identifies
all opinion words and phrases. Each positive word is assigned
the opinion score of +1, each negative word is assigned the
opinion score of -1, and each neutral word is assigned the
opinion score of 0. For example, we have the sentence, “The
picture quality of this camera is not great, but the battery life
is long.” After this step, the sentence is turned into “The
picture quality of this camera is not great [+1], but the battery
life is long [0]” because “great” is a positive opinion word
and “long” is neutral or context dependent.
Negation words and phrases are used to revise the opinion
scores based on some negation handling rules. For example
“The picture quality of this camera is not great [-1], but the
battery life is long [0]”.
Strength of Opinions: Besides detecting the polarity of
opinions as positive, negative or neutral, it is equally
important to determine the strength of the opinions present in
the reviews. Opinions with words like “very”, “too” before
them, need to have a higher orientation than those that don’t
have. Example: “The camera is good.” Here, good has a
positive orientation of +1.But a sentence such as “The camera
is very good.” should have an orientation of greater than +1
because the word “very” adds a certain amount of strength to
the word “good”.
Fig. 1 Final Summary and Graph
Taking into account the positive as well as the negative score
for the overall product, a recommendation is provided to help
the willing-to-buy customer to make a decision.
2.4.Opinion Aggregation and
Summarization
This step applies an opinion aggregation function to the
resulting opinion scores to determine the final orientation of
the opinion on each object feature in the sentence. After all
the previous steps, we are ready to generate the final featurebased review summary.
Fig. 2 Final Recommendation
For each discovered feature, related opinion
sentences are put into positive and negative categories
according to the opinion sentences’ orientations. A count is
computed to show how many reviews give positive/negative
opinions to the feature.
All features are ranked according to their aggregate scores.as
follows:
𝐴𝐺𝑅(𝐹) = 𝑇𝑃𝑆(𝐹) − 𝑇𝑁𝑆(𝐹)
(1)
Where TPS (F) is the total positive score for a particular
feature F, TNS (F) is the total negative score for the same
feature F and AGR (F) is the resultant aggregate score. Then a
summary is generated which comprises of total no of positive,
negative and neutral opinions for each of the features and each
of the feature is ranked according to descending order of
aggregate value.
3. OBSERVATION
3.1.For Pre-processing Phase
During stop-word removal we usually use a known stopword list for removing frequently occurring unimportant
words. The words such as “the”, ‘a’, “not”, “but”, “an” occur
frequently in the corpus and are unimportant and hence should
be treated as stop-words. But since we deal with natural
language here, we need to refine the known stop-word list as
there are words such as “not”, ”but”, ”and” which have a
serious impact on opinion orientations.
3.2.For Feature Identification and
Extraction Phase
Nouns should be identified using POS tagger [2]. Their
occurrence frequencies are counted, and only the frequent
ones are kept. A frequency threshold can be decided
experimentally [2].
In our model of opinion mining, the threshold value is
entered by the user. Only those features will be displayed
whose cross document frequency is greater than the threshold
value selected by the user. The reason for using this approach
is that when people comment on product features, the
vocabulary that they use usually converges, and most product
features are nouns. Thus, those nouns that are frequently
talked about are usually genuine and important features.
The POS tagger tags a word as noun (NN), verb (VB), and
adjective (JJ) based on the current sense in the sentence. For
example “Signals from the boat suddenly stopped.” Here
signal is a noun. “He signed his disapproval with a massive
gesture.” Here it means a verb. “The year saw one signal
triumph for labour party.” Here it means an adjective.
As a result of above consequences, the same word appears
in the noun list as well as adjective adverbs list also. Hence
there is a high probability of generation of false feature
words.
False feature words are the frequently occurring nouns
which are not actually feature words. The noun words were
extracted with their frequencies across the documents in the
corpus and different values for threshold were tried. But, it
was observed that along with the actual features, false features
also enter the list.
Fig. 4 Graph showing only Feature words
3.3. For Opinion-Feature Identification
The research has shown that Phrases containing adjectives
or adverbs are good indicators of subjectivity and opinions
[2]. Accordingly, for construction of opinion lexicon the
adjectives and adverbs from POS tagged documents were
extracted.
Besides detecting the polarity of opinions as positive,
negative or neutral, it is equally important to determine the
strength of the opinions present in the reviews. Opinions with
words like “very”, “too” before them, need to have a higher
orientation than those that don’t have. Example: “The camera
is good.” Here, good has a positive orientation of +1.But a
sentence such as “The camera is very good.” should have an
orientation of greater than +1 because the word “very” adds a
certain amount of strength to the word “good”.
3.3.1.
Comparative Study:
Fig. 3 Graph Showing False Features
Therefore, after POS tagging is done, we extract nouns and
compare each noun with some reference source to determine
if the noun is a feature or not.
Thus, the following graph shows all the features with their
cross document frequency i.e. the number of users expressing
their opinion on that particular feature. The white line
indicates threshold equal to 50 where only the features above
the threshold are selected for further processing.
Fig. 5 Graph of Feature v/s Aggregate without considering
the strength of opinions
The following graph shows the relation between the
aggregate score and the individual positive, negative and
neutral score. It is observed that the FAS (f, D) is
independent of the neutral opinions. Consider battery which
has 16 positive opinions and 13 negative opinions. With this
aggregate is calculated as 3 i.e. aggregate=positive – negative.
This shows that the aggregate value is independent of neutral
opinion.
Fig. 6 Graph of Feature v/s Aggregate considering the
strength of opinions
As seen from the above two graphs, the features observed
for a particular threshold are the same. But the aggregates in
fig 5 are higher than those in fig 6. Thus by considering the
strength of the opinions the weightage of the feature strongly
increases. Our observation is that the strength of opinions
gives a better aggregate score.
3.4. For Aggregation and
Summarization Phase
It is observed that final aggregated score is directly
dependent on number of positive and negative reviews and is
independent of the neutral reviews. Thus, the feature with the
highest score will be the best feature of the product. The
following relation is observed:
FAS (f, D) = TPS (f, D) – TNS (f, D)
(2)
Where, FAS (f, D) is Final Aggregate Score for a feature f
in corpus D. TPS (f, D) is Total Positive Score for a feature f
in corpus D, and TNS (f, D) is Total Negative Score for a
feature f in corpus D.
When strength is considered, it is observed that TPS does
not give the actual number of users having positive opinions
about the feature. But, we have noticed that when strength is
considered, the quality of scores improves. As a result, we can
say that the aggregate also improves.
Fig. 7 Relation Graph for 500 documents(corpus 1)
In the following graph, battery has 6 negative opinions and
2 positive opinions, thus the aggregate score is -4. Also
button has aggregate score -1. This shows that battery and
button are bad features of the product.
3.
4.
5.
6.
7.
8.
9.
This model is done considering only one model of the
digital camera. As a future scope, it can be extended to
consider models of camera of different brands and
perform opinion mining on each of these, thereby
recommending the best model out of all.
As a part of future scope, we can have a functionality to
find the opinion spam i.e. detection of illegitimate,
unauthentic and false opinions about the product, only
retaining the legitimate opinionated sentences.
Further, comparative sentences can also be processed.
Currently, our model of opinion mining works with
Natural Language Processing tools like Part of Speech
Tagging and Rule based Analysis. Other alternative
means data mining techniques like Bayesian belief
network, neural network can also be used.
For feature extraction, instead of capturing features based
on exact word, fuzzy logic could be used.
The current model of opinion mining works with only a
few basic Natural Language Rules like rule for but
clause, not clause, and clause, but also and so on. In
order to have more accuracy, the system can be trained
with more natural language rules eventually increasing
the accuracy. However, 100% accuracy is impossible
since English is a free flow natural language and working
with NLP is still a challenging task.
Opinion mining can be done on other languages with
Devnagri scripts or even based on speaking styles and
accent followed in different regions across the world.
6. REFERENCES
[1] Bing Liu, Phillips S.Yu, Xiao Wen Ding. KDD'04, 2004,
A holistic approach to Opinion Mining,.
Fig. 8 Relation Graph for 126 documents (corpus 2)
4.
[2] Bing Liu. Sentiment analysis and subjectivity, Handbook
of Natural Language Processing, Second Edition, 2010
CONCLUSION
The model for opinion mining using NLP approach is created
successfully, which provides a summary of the opinions on
each individual feature of the Digital Camera. As a part of
summary it provides the mostly opinionated features, their
aggregate scores and the number of positive, negative and
neutral opinions on each feature. It also provides summary in
a graphical form and finally provides the recommendation
page for the willing-to;buy customer.
With NLP approach alone however, 100% accuracy is
impossible since English is a free flow natural language and
working with NLP is still a challenging task. Some issues like
false feature words, strength of opinion need to be considered.
[3] M.Hu and B.Liu. KDD’04,
summarizing customer reviews.
2004,
Mining
and
[4] Yee W Lo .Review Mining & Sentiment Classification in
Social Networks, IEEE.
[5] L. Zhuang, F. Jing, X.-Yan Zhu, and L. Zhang, CIKM-06,
2006, Movie Review Mining and Summarization.
[6] .N. Jindal, and B. Liu, In AAAI’06, 2006, Mining
Comparative Sentences and Relations.
[7] B. Liu, Springer, 2006, Web Data Mining: Exploring
Hyperlinks, Contents, and Usage Data.
5. FUTURE SCOPE
1.
2.
In this model, the opinion mining is carried out in the
light of known feature list and a known set of synonyms
[1]. Dynamicity can be achieved when both the feature
list and the synonyms for the feature are dynamically
generated which is still a challenging task.
This model takes as input the text corpus where each
document is a text review extracted manually. An
automated mechanism for extraction of text reviews from
various review sites could be incorporated i.e. a Scrapper
could be created which will dynamically extract all the
reviews from the review sites and store them in text files.
7. WEBSITES
1.
http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
2.
http://www.cis.upenn.edu/~treebank/
3.
http://sentiwordnet.isti.cnr.it/
4.
http://videolectures.net/wsdm08_liu_hlbaom/