A Study on Brand Positioning using Perceptual
Mapping through Sentiment Analysis: A Case of
Automobile Industry
Kalyan Sengupta1, Deepa.M2 , Arnab Gosh3,Saujanya Wagle4
1(Professor, Management, IFIM Business School, India)
2(Management, IFIM Business School, India)
3(Management, IFIM Business School, India)
4(Management, IFIM Business School, India)
Abstract: Millions of customer opinions on internet, text analytics has changed the way the business
operates. Social and sentiment analysis helps in figuring out the emotions of the customer attached to a
brand without much effort. This supports the manager to improve his company’s brand in the minds by
either sharpening their strength or acting upon the weakness to retain and gain customer. In this paper we
have taken an initiative to understand what customer perceive about the top brands in automobile industry
(Fortune 500 list). Using social and sentiment analytics, we have tried to find the polarity of each
parameters associated with the brand to build a perceptual map. Through this managers can understand
each brand’s positioning in the minds of the customer as well as market gap and act on it to make their
brand strong.
Keywords: Sentiment Analysis, Perceptual Mapping,
1. Introduction
The volume of data is increasing at exponential rates every data. Almost all type of organization is storing their
data electronically. 90% of the data in the world today has been created in the last two years alone. Currently, the
generation rate of data is roughly around 2.5 quintillion bytes per day. The amount of text, flowing over the
internet, is in the form of digital libraries, repositories, and other textual information such as blogs, social media
network and e-mails.
It is challenging to determine the appropriate patterns and trends from this large volume of data to gain knowledge
in the certain study of the field. Text mining is a process to extract interesting and significantly analyze the patterns
to explore knowledge from textual data sources. It is a multi-disciplinary field based on information retrieval, data
mining, machine learning, statistics, and computational linguistics. There are several text mining techniques like
summarization, classification, clustering which can be applied to extract knowledge.
Traditional data mining tools are incapable to handle textual data since it requires a lot of time and effort to extract
information as textual information is in the form of unstructured data. A lot of data cleaning is required. Text
mining techniques are continuously applied in industry, academics, web applications, internet and other fields.
There is wide a range of application of text analytics in areas like search engines, customer relationship
management system, filter emails, product suggestion analysis, fraud detection and social media analytics use text
mining for opinion mining, feature extraction, sentiment, predictive, and trend analysis.
Online car review websites have become an extremely popular platform for sharing car related information, with
a large number of reviews being posted daily. Car review websites such as AutocarIndia and topgear.com have
turned into very important resources for car lovers and buyers. Car reviewers use these forums to solicit their
feedback and car-lovers as well as buyers use them to decide on which car to buy or share their experience. A car
review websites, such as TopGear, enables reviewers to contribute descriptions, pictures, reviews and travelogues
about the car. Individuals who visit a TopGear website can gather numerous bits of information about the price,
quality, specification such as mileage, RPM, etc. It is also an online social network, enabling buyers to connect
and be connected with other car lovers or buyers around the world, to provide and read feedback/reviews on the
satisfaction level they feel about the car. Content created by car lovers on cars has turned out to be increasingly
essential. For instance, a recent study found that more than 60% of respondents checked online surveys, web
journals, and other car reviewer's criticisms before purchasing an item or service, and an additional 20% said the
reviews have a significant influence on their purchase decisions1. Companies usually collect customer feedback
directly as well as us from these websites to know their car standards. Furthermore, online media generally
generate larger volumes of data, and as such opinion mining analyses of car, reviews would be able to give a
larger and better picture in terms of reviews on a given car. This will also ensure that each relevant factor can be
studied and not missed out, thus potentially increasing the chance of knowing what customer really wants from
the manufactures at a given price. Text analytics can show details such as which brands in industry speak the
loudest, who is talking about which brand, products, industry, competitors, and what topics in an industry would
generate the most buzz . To have a competitive edge over other brands, the solution is to tap on all the information
in the reviews, to understand customer’s experiences, needs and wants, and to identify factors that affect customer
satisfaction.
This paper reports on a text mining approach that allows for the extraction of meaningful patterns from large
volumes of textual information and what customers feel about the existing cars at a certain price range. Using this
analysis we can come to a conclusion of what customers really look for is it really provided by the manufacturers
and if yes, by brand positioning map we can find out which brand provides the same. This research will tell the
car manufactures what they are good at and in which area they really need to concentrate on to acquire and retain
customers.
2. Literature Review
Sentiment analysis is considered one of the most popular applications of text analytics. The primary aspect of
sentiment analysis is data analysis on the body of the text for understanding the opinion expressed by customers
and other key factors comprising modality and mood. The process of sentiment analysis works best on text that
has a subjective context than on that with only an objective context. This is because when a body of text has an
objective context or perspective to it, the text usually depicts some normal statements or facts without expressing
any emotion, feelings, or mood. Subjective text contains text that is usually expressed by a humans
(customers/reviewers) having typical moods, emotions, and feelings. Sentiment analysis is widely used, especially
as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to
understand its reception by the people and what they think of it based on their opinions.
The automobile industry thrives on customer satisfaction, as that would lead to repeat purchases as well as good
word-of-mouth promotions. The industry also depends a lot on customer feedback to improve their car design
according to the needs and wants of the customer. This would require the gathering of data from clients, customers,
reviewers, online websites, magazines and its analysis. In the early days, getting data was through manual means,
such as comment cards, surveys, reviews, articles, site visit reports, etc3,4. All these information has to be manually
perused, analysed, and some conclusions drawn. This exercise is very tedious and time consuming, and the data
gathered is usually quite smaller in volume. With the advent of the Internet, much more data is generated and is
quite readily available for extraction as such the survey of the product is already been done. This has made
customer more informed about a particular brand even before they decide buying a particular product. As a result,
the customer’s buying choice that is the range of alternatives to buy has expanded many-fold, and this would
usually drive the customers to seek advice from various parties such as reviewers, users, and opinion experts to
make the final choice. On this point, it has been found that this decision-making is very much influenced by online
networking, where many reviews of products and services are shared by netizens3,4. With the rise of online review
websites, the car industry is said to be strongly affected by eWOM (Electronic Word-of-Mouth).
Sentiment analysis is considered one of the most popular applications of text analytics. The primary aspect of
sentiment analysis is data analysis on the body of the text for understanding the opinion expressed by customers
and other key factors comprising modality and mood. The process of sentiment analysis works best on text that
has a subjective context than on that with only an objective context. This is because when a body of text has an
objective context or perspective to it, the text usually depicts some normal statements or facts without expressing
any emotion, feelings, or mood. Subjective text contains text that is usually expressed by a humans
(customers/reviewers) having typical moods, emotions, and feelings. Sentiment analysis is widely used, especially
as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to
understand its reception by the people and what they think of it based on their opinions. Sentiment Analysis is
considered one of the most attractive fields that encourage to study and apply in various sectors. In this paper,
sentiment analysis models are applied on three of most leading automotive industry companies to extract the
polarity and opinions of customers around each company, which are very useful information that helps in
marketing. Analysing how customers feel about a particular brand is good but then without analysing what makes
the customers feel good or bad about a particular brand, will not help the manufactures to understand what features
should be retained and what to be added.
With the explosion of Web 2.0 platforms, social media sites become a huge source for consumer voices. Capturing
and analysing public opinions from social media sites has recently enjoyed a huge burst of research activity. One
of The resulting emerging fields is sentiment analysis5. In paper6, the authors analysed three of the most popular
companies in pizza industry by using text mining. The authors studied information from social media sites about
the users of those companies and their competitors. The goal was to help those companies improve their services
and strategies to attract more customers. They found that social media sites have an important role in creating
competitive advantage.
Since the rise of internet, a huge number of data has been gathered. Internet has been the collection of the huge
amount of data which keeps on expanding daily. Data is growing 40% annual compound rate reaching nearly 45
ZB5.
In 2014, there were 2.4 billion internet users. That number grew to 3.4 billion by 2016, and in 2017 300 million
internet users were added – making a total of 3.8 billion internet users in 2017 (as of April, 2017) which is a 42%
increase in people using the internet in three years.
Since the rise of social media there has been a huge rise in the personalization of internet and rise of personal data
collection. Around 1,209,600 new data producing social media users each day, 656 million tweets per day, More
than 4 million hours of content uploaded to Youtube every day, with users watching 5.97 billion hours of Youtube
videos each day. 67,305,600 Instagram posts uploaded each day96.
As more and more people have joined social media, their voices are heard among the masses. Similarly, it has
been easier for the people to voice their opinion on various products, services, political opinions etc.
This review can be a huge benefit for the companies to know about the service or the product they are providing.
It is becoming a common practice for a consumer to learn how others like or dislike a product before buying, or
for a manufacturer to keep track of customer opinions on its products to improve the user satisfaction. However,
as the number of reviews available for any given product grows, it becomes harder and harder for people to
understand and evaluate what the prevailing/majority opinion about the product is.
These data could be divided into both structured and unstructured data. While most of these data are in the
unstructured format. Lean, Wang and Lai (2005) in their paper quoted a survey done by Delphi Group which said
that around 80 per cent of the data are stored in an unstructured manner9.
Sentiment classification, also known as affect or polarity classification, attempts to address this problem by (i)
presenting the user with an aggregate view of the entire data set, summarized by a label or a score, and (ii)
segmenting the articles/text-fragments into two classes that can be further explored as desired.
According to a recent survey that was conducted by DoubleClick (2005), most of consumers did an online search
before making their purchase. Moreover, prior studies have revealed that sentiments in online product reviews
have a major influence on consumers’ buying decision10. Zhu and Zhang (2006) explored the impact of online
consumer reviews on the demand for personally encountered goods, and made alike inference. Since automobile
sector is usually experience related, notions in online reviews should have a strong reach to customers.
As mentioned, there is right away an enormous amount of online reviews on automobile blogs which is beyond
the imagination of humans. Thus, there is an immediate need for innovation that can spontaneously break down
the attitudes of customers in their reviews. Fundamentally, the job of instinctively analysing the online reviews
can be performed by sentiment classification (sentiment analysis or opinion mining)3,18,14. Mining sentiments from
reviews on internet, although, is a complex method that requires more than just text mining methods. There is
problem in couple of issues. Firstly, database of customer reviews are to be pulled from websites, where search
engines can be used. Then the data of reviews are need to be separated from non- reviews. After that sentiment
classification process can be conducted. Pang etal. (2002) found text mining algorithms on traditional topic-based
categorization are better than sentiment classification algorithm. Keywords are used to recognise the topics but
sentiment would be expressed in a more subtle manner because classification of sentiment needs more
understanding than normal topic based classification18. The goal of Sentiment classification is to extract the text
of written reviews of customers for products or services by grouping the reviews into positive or negative notions
according to the polarity of the review11. After the sentiment classification consumers can use the result to know
the information about the product and can easily identify the best and the worst of them. It would help consumers
to differentiate products of different brands and help them to decide which product according to their requirement.
Products of same category differs from brand to brand which creates confusion among customers but with the
help of sentiment classification consumers can compare quality wise, feature wise , specification wise and
pricewise with different brands as there are wide range of products available in market.
With wide adoption of technology, sentiment classification of reviews has become crucial for companies as it
helps them to identify the defects and need in product improvement. This method is not limited to specific
industries. It has been implemented in many domains such as movie reviews, product review, customer feedback
reviews, legal blogs and travel blog14,15,16,17.Many other applications includes extracting reviews from discussion
forums such as twitter, blogs etc. provide useful statistical data to make sentimental analysis systems for particular
products or services. Buying car, would be one of the good application as it’s a huge investment and customers
are very cautious before buying any vehicle. For opinion mining applications, machine learning and semantic
orientation techniques have been utilized in the extant literature18.In the machine learning approach mostly
supervised learning and classification techniques are used for opinion mining.
In the semantic orientation approach ‘‘unsupervised learning” is used for opinion mining as it does not need to
train machine to mine the data. It measures how much a word is inclined towards positive and negative. The
potential of on social and sentiment analysis is very huge. When it comes to analysing unstructured data sets, a
range of methodologies /are used. Natural language processing (or NLP) is a component of text mining that
performs a special kind of linguistic analysis that essentially helps a machine “read” text. NLP uses a variety of
methodologies to decipher the ambiguities in human language, including the following: automatic summarization,
part-of- speech tagging, disambiguation, entity extraction and relations extraction, as well as disambiguation and
natural language understanding and recognition.
To work, any natural language processing software needs a consistent knowledge base such as a detailed
thesaurus, a lexicon of words, a data set for linguistic and grammatical rules, an ontology and up-to-date entities.
Hence further boosting the ability of text mining and social and sentiment analysis. In the context of this research
All Information in the world can be broadly classified into mainly two categories, facts and opinions. Facts are
objective statements about entities and worldly events. On the other hand opinions are subjective statements that
reflect people’s sentiments or perceptions about the entities and events. Maximum amount of existing research on
text and information processing is focused on mining and getting the factual information from the text or
information.
By comparing the sentiments on specific subjects between uniform intervals we can detect opinion trends. By
comparing sentiments for specific subjects with other subjects, we can do a competitive analysis. For example,
we can do a quantitative analysis by counting the numbers of positive and negative sentiments to see if a subject
is on balance favourable or unfavourable. It may be useful to analyse changes in the balance over some period of
time and to compare it with other subjects. The output of our method also allows us to do qualitative analysis
easily because it provides very short summaries of the sentiment expressions.
In this paper, we discuss issues of sentiment analysis in consideration of related work and define the scope of our
sentiment analysis in the context of various reviews obtained from the internet. This paper is carried out as no
further studies were conducted on the text reviews of automobile industry. The automotive industry in India is
one of the largest in the world with an annual production of 23.96 million vehicles in FY 2015–16, following a
growth of 2.57 per cent over the last year. The automobile industry accounts for 7.1 per cent of the country's gross
domestic product.
Thus, text analysis of reviews of products in this huge industry would be the goal of our current paper. Sentiment
analysis would be done on various products in this industry in the context of Indian market. As, this kind of
research has not been conducted and no significant paper on this particular topic is available.
3.
Medhodology
For doing any research first and foremost thing is data. When we analysed our problem and while searching
through the internet, we got a dataset from a reputed website called Kaggle. This website publishes only
authenticated and authorized datasets. As reliability is high and the dataset is relevant to the research, we decided
to take this and did secondary data research.
Once the dataset is finalised now comes the data cleaning process. Though the data looked clumsy initially when
put on excel but when it was opened in KNIME the data cleaning was easier since the software was easier and
powerful to handle unstructured data.
Now that the format of the dataset has been changed next step was to find the subjectivity and polarity of the
reviews. We wrote a python code for finding the subjectivity and polarity of each review so as to reduce the
biasness when it comes to find the subjectivity and polarity for the entire dataset.
Python code was used to find the subjectivity and polarity. CSV is a library used in python for importing and
exporting spreadsheets and databases. TextBlob is a library used to for processing textual data. It provides a simple
API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase
extraction, sentiment analysis, classification, translation, and more. By this we have calculated the subjectivity
that is how much the textblob understood the reviews and the polarity of the review on scale of 0 to 0.5. Before
we take the average of all the values, we have to find the most used and relevant words so that we get closer to
accuracy [1].
To find this, we have used KNIME software because the software gives a platform to create visual workflows
with an intuitive, drag and drop style graphical interface, without the need for coding. It is an open source software
for creating data science application and services. Excel Reader read the spread sheet containing customer reviews.
Strings to Document node helped in converting the specified strings to documents. N Chars Filter, filtered all
terms contained in the input with less than the 3 characters.
Stop Word Filter, filtered all terms of the input documents, which is in the specified stop word list and/or in the
second input table. POS Tagger assigns to each term of a document a part of speech (POS) tag. Bag of words node
creates a bag of words (BoW) of a set of documents. A BoW consists of at least one column containing the terms
occurring in the corresponding document.TF node computes the relative term frequency (tf) of each term
according to each document and adds a column containing the tf value. The value is computed by dividing the
absolute frequency of a term according to a document by the number of all terms of that document. Frequency
filter, filters terms in the given bag of words with a certain frequency value. On the other hand minimum and
maximum values can be defined to be used for filtering. If the value of a specified frequency column is less than
the minimum or greater than the maximum value the term is filtered. A tag cloud is a representation of words
indicating the importance of the words by manipulating the visual properties 19.
The ultimate aim of the processing is to make a word cloud to find out the most frequently used words by the
customers while expressing their views. Word cloud is basically a visual representation of important text (tag) in
a given document. Each car brand reviews is processed by these steps and word cloud is formed. Now most
frequently used words are taken into consideration to filter out the reviews containing those words so that the
polarity can be calculated accurately.
Customer while buying a product will search for the desired attributes. Only when his/ her needs are mapped to
the attributes of a product, product will be sold. In the automobile industry, the most desired features which
customers look for while buying is Seating, Engine & Performance, Safety, Infotainment, Comfort &
convenience, Storage & cargo carrying capacities, New vehicle & powertrain warranty.
Now in each attributes there are sub-attributes which can be clustered together as a main attribute. Here are the
major attributes under which these sub attributes can be clubbed:
• Seating: Bench or bucket seats, split folding rear seat, 4, 5, 7 passenger, leather, heated seats, electronic vs.
manual adjustments armrests
• Engine & performance features: Engine, transmission, horsepower, towing, fuel mileage
•
Safety features: Air bags, traction control, lane departure warning, back up camera
• Infotainment features: USB, Bluetooth, Wi-Fi, touch screen, iPhone compatible, surround
sound, CD player, navigation system
• Comfort & convenience: Cruise control, power windows, power seats, power mirrors, outlets for cell phone
charging or other devices, cup holders
• Storage & cargo carrying capacities: Cargo systems, box liners, tonneau covers
• New vehicle & powertrain warranty: Length, deductible, length of roadside assistance & courtesy
transportation coverage, exclusions20.
According to this general perception we have categorized 3 main attributes: Comfort, Price and Performance.
Under this main attributes we have clustered sub attributes which we found important such as:
Comfort: Seat, Storage, Luxury, Quality
Price: Range, Cost, Value, Expensive, Economical
Performance: Engine, Mileage, Suspension, Transmission, mph
From this we have done manual topic modelling. For each car brand now we are trying to filter the reviews under
these 3 aspects. After filtering we are calculated the average polarity and subjectivity values for each car brand.
Using these results now we have analyzed the car brands.
4.
Results
Fig2 : Polarity values of brands taking only comfort into consideration
All the reviews that revolved around comfort was further fillerted and used for this analysis. The sentiment of
those reviews for each car were calculated to understand the sentiment of the customer for that car in terms of
customer. Using TextBlob package from python, we were able to calculate the sentiment of each reviews and
those score were averaged out to find out the overall sentiment in terms of comfort. The score range from 0.0 to
0.5. Where 0.0 has negative sentiment and 0.5 has positive sentiment.
Here in the plot above Fig 2, we can clearly see that the positive sentiment towards Ferrari was relatively very
high whereas Landrover was low. Whereas other cars, we can see the sentiment of customer towards comfort was
neutral.
Hence, reviewers are talking positively about the comfort attribute of Ferrari meanwhile they are talking
negatively about the price of Landrover
Fig3: Polarity values of brands taking only performance into consideration
All the reviews that revolved around performance of car was further filtered. The sentiment of those reviews for
each car were calculated to understand the sentiment of the customer for that car in terms of its performance.
From the graph Fig 3. above we can see that the customer’s positive sentiment towards Ferrari was quite high.
Whereas performance of Mercedes and Porsche was neutral. Finally we can see the sentiment of customer towards
Mini, Tesla and Volkswagen relatively less than other neutral sentiment.
Hence, reviewers are talking positively about the performance of Ferrari meanwhile they are talking negatively
about the price of Mini, Tesla and Volkswagen.
Fig4 : Polarity values of brands taking only price into consideration
All the reviews that revolved around price of car was further filtered. The sentiment of those reviews for each car
were calculated to understand the sentiment of the customer for that car in terms of its price. Here we can clearly
see that in fig4 the only positive sentiment was received for Ferrari in terms of price. Whereas we see average
sentiment of Mercedes, Porsche and Tesla towards price. But when we go to Landrover and Mini, it has very less
sentiment score.Hence, reviewers are talking positively about the offering price of Ferrari meanwhile they are
talking negatively about the price of Landrover and Mini.
Fig.5 represents the positioning of the brands based on Price and Comfort. Here the polarity of the sentiments of
the reviews are taken as features which talk about price and comfort of the vehicles. It is observed that Ferrari is
in the top position where people gave mostly positive reviews in both aspect of price and comfort. Next the brands
Porsche and Mercedes seems to partially overlap from which we can conclude that these two brands are highly
competitive. Coming to the brands Volkswagon and Mini Cooper we can interpret that people talking more
positively on comfortability than price. But its not same with the Tesla as people gave more positive comments
than comfortability which concludes that Tesla are providing their product at reasonable price. From the position
of Landrover which is positioned lowest
Fig5 : Price vs Comfort
Fig.6 represents the positioning of the brands based on Price and Performance. Here the polarity of the sentiments
of the reviews are taken as features which talk about price and performance of the vehicles. Again, Ferrari is at
the top position with most positive reviews when both Price and performance are talked about. The same position
can be seen for Porsche and Mercedes being competitive but this time performance is creating slight difference
making the position of Mercedes little higher than Porsche. It seems performance is the differentiator in the
Mercedes when compared to Porsche. After this we can see a huge difference in the position of the Landrover
where it was positioned at lowest in the Comfort vs price graph but now it is above the three brands. From this it
can be concluded that Landrover is known for its performance. The brand Volkswagon and Mini Cooper are at
the bottom of the positioning which indicates they need to work out in the improvisation in the performance of
their cars.
Min
i
Fig6 : Price vs Performance
Fig 7 : Performance vs Price vs Comfort
Fig.7 represents the 3-dimensional positioning of the brands based on Comfort, Price and Performance. Here the
polarity of the sentiments of the reviews are taken as features which talk about comfort, price and performance of
the vehicles. Ferrari can be concluded as the best car being in the top position in all parameters. Next comes the
brands Porsche and Mercedes being overlapped in the same position where performance giving edge to Mercedes.
This position can be considered as the market captured by both Mercedes and Porsche. Landrover capturing fourth
position when all the features taken at a time but shows low position when talked about comfortability. So,
customers needed to choose this car based on their requirement. If they are looking for comfortability, they
shouldn’t go for it but if any customer looking for performance and price then it’s their car. After this comes Tesla
which is positioned averagely. At the end Mini Cooper and Volkswagon are positioned low as compared to other
brands and are close to each other.
5.
Managerial Implication
Perceptual mapping will help the managers to understand the market segments they are operating in as well as
their competitors in that segment and in the other segments. It also gives a clear picture of how the target market
really perceives the brands in the marketplace, is it same as the brand intent too and weather our brand has a clear
positioning space in the market. Market gaps can be identified and can be seen as an opportunity to be a monopoly
in some cases.in this paper we have done the analysis of top brands in automobile industry according to Fortune
500 companies list and made a perceptual mapping. In conclusion, we have given the consolidated points which
could help the managers of these brand to know their customer's perception about the product.
6.
Conclusion
Customers love to voice out their opinions through various platforms in the form of comments. Perception of a
brand could be understood through those comments. Now a days, with a heap of data available on customer voices,
there is no need to rely on the traditional medium of data collection. Using sentiment analysis, we can get the
score through which perceptual mapping is done and can understand the customer’s mind. This is an important
aspect for a company to understand what is there in the minds of the customer, through which they can then
concentrate on the shortcomings as well as improvise the current scenario. Through this analysis we found that
Ferrari is doing well in all three aspects (Price, Comfort and Performance) and Landrover must improve in aspects
of price and comfortability. While doing perceptual mapping, we found a market gap. There is no company
operating in the region of high performance with reasonable price and high comfort with reasonable price. If any
company can grab this opportunity to manufacture cars for this segment with profit, they would be the market
leaders. This is how we can use social and sentiment analytics and managerial knowledge to understand the market
and perception of users to come up with news strategies or product to be a market leader.
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