Automated Marketing Research Using Online Customer Reviews

Automated Marketing Research Using Online Customer
Reviews
Thomas Y. Lee and Eric T. Bradlow
Web Appendix A. Algorithmic Details
In this Appendix, we elaborate on specific details of the text-processing algorithms.
[1] Data collection and pre-processing
After identifying the source of reviews, we wrote a program in the Python programming
language. For each review, we get the product identifier used by Epinions.com to uniquely identify a
product, the list of Pros, and the list of Cons. Product brand names are excerpted from the Epinions.com
product identifier 1 and inserted into a MySQL database. It is also important to note that one could
separate the selected reviews by pre-defined segments (i.e. demographic clusters, and hence produce
segment-level Pro-Con lists that would be analyzed distinctly), or attempt to (but is beyond the scope of
this research) to simultaneously infer latent segments and market structure simultaneously.
To construct the matrix of word vectors, we focus on each phrase. For now, we do not
distinguish between whether a phrase appears as a Pro or a Con, focusing only on grouping together those
phrases that discuss a common product attribute. As a standard preprocessing step in text mining, we
normalize words as follows. Delete all stop-words and stem the remaining text (Salton and McGill 1983).
Stop-words, like grammatical articles, conjunctions, prepositions, etc. are meaningless for purposes of
product attribute identification so they are removed. For example, after pruning, the phrase "Only 8 mb
Smart media card included" becomes "8 mb Smart media card included." Reduce words to their root
form by stemming. We use the Porter stemmer to find equivalences between singular, plural, past and
present tense forms of individual words used by customers. Thus, "includes" and included" are both
reduced to the root "includ."
[2] Vector space model and word importance

Borrowing from the information retrieval community, our phrase � word matrix is a
representation of the vector-space model (VSM). More formally, j � J is a word in the set of all words; i
� I is a phrase. A phrase is simply a finite sequence of words and J is a subset of the set of finite word
sequences I = {| j � J}. We define an initial phrase � word matrix as a simple variation on the term-
frequency inverse-document-frequency (TF-IDF) VSM (Salton and McGill 1983):
Matrix(i,j) = (TFij � IPFj) (A1.1)
where the term frequency �TF ij � counts the total number of occurrences of word j in the instances of
phrase i. The inverse phrase frequency IPFj = log(|I|/nj) is a weighting factor for words that are more
helpful in distinguishing between different product attributes because they only appear in a fraction of the
total number of unique phrases. If |I| represents the total number of unique phrases in the review
collection, nj counts the total number of unique phrases containing word j.
A limitation of the TF-IPF weighting is that there are still some terms (e.g. sentiment words like
"great" or "good") that are neither stop words nor product attributes yet appear with product attributes in
the TF-IDF matrix. As an additional discount factor beyond IPF, we automatically gather words from a
second set of K phrases using online reviews for an unrelated product domain. Intuitively, words
appearing in the reviews for unrelated products are less likely to represent relevant product attributes for
the focal one. For example, words describing digital camera attributes are less likely to also appear in
vacuum cleaner reviews.
Formally, for a set of (I') phrases drawn from the set of finite word sequences over j � J, we
calculate rank(j) = rank(TF'ij�IPF'j) where higher weighted frequencies correspond to higher rank. Note
that multiple words may share the same rank; if we define words that do not appear in any phrase as
having IPF'j = 0, then we may say:
Matrix(i,j) = TF rank�
j��
�IPF � IPF'
�
ij
� (A1.2)
Thus, we scale TF by the rank of the word in the unrelated product domain and scale the IPF by IPF'
j
j
2

[3] Phrase clustering
We cluster the vectors (the matrix rows) so that all phrases describing the same product attribute
are grouped together. More formally, given the phrase � word matrix(i,j) over the set of I phrases and the
set of words J, we seek to separate phrases into a set C of k mutually exclusive and exhaustive clusters
We use the cosine measure of angular distance between vectors to calculate similarity. The cosine
measure is then applied to the phrase � word matrix using the K-means clustering algorithm. As noted
earlier, while any number of clustering algorithms is acceptable, we selected K-means for its simplicity
and its familiarity to both the text-mining and marketing communities.
The quality, QC, of a K-means clustering, C, is calculated by the sum of the distances from each
vector in a cluster to that vector's centroid. Following (Zhao and Karypis 2002), this metric is more
simply defined as the sum of the length of the composite vectors:
�v centroid�c
��
� composite�c
�
� � cos
i �
QC �
,
�ci�C�v�ci�ci�C where composite �ci � � �v (1)
�v�
Because K-means is known to be extremely sensitive to its initial conditions, we repeat the algorithm ten
times, beginning with a new, random set of k centers and pick the solution that maximizes QC.
[4] Attributes and their dimensions
A critical step in our approach is to discover not only what product attributes customers are
discussing but also the granularity with which those attributes are discussed. Specifically, we seek to
elicit the attribute dimensions that customers use in their reviews. To discover attributes, we assume that
each phrase corresponds to a distinct product attribute. To discover attribute dimensions, we will assume
that each word in the phrase corresponds to a distinct dimension. Discovering attributes then reduces to
the assignment of particular words to attribute dimensions.
ci
i
4

Conceptually, we model this process as a constrained optimization problem. Abusing our
previous notation slightly, assume a set of phrases I composed from the set of words J and a set of
attribute dimensions D. We have J � D binary decision variables Xjd where Xjd is 1 if word j is
assigned to dimension d. There are I � J {0, 1} variables representing a constraint matrix where Yij is
1 or 0 depending upon whether word j appears in phrase i. Thus, our objective is to:
max
s.
t.
� X
�
�i Y * X � 1
X
jd
jd
J
ij
binary
The graph partitioning algorithm used to set the parameters I, J, and D and the constrained logic
program (CLP) by which we solve the optimization are implemented in Python and detailed next.
[5] Graph representations
To discover attributes, we assume that each customer review phrase corresponds to a distinct
product attribute. To discover attribute dimensions, we assume that each word in the phrase corresponds
to a distinct dimension. Discovering attribute dimensions then reduces to the assignment of particular
words to attribute dimensions. But how do we know how many dimensions there are in the assignment
problem? Is it possible that the assignment optimization has no feasible solution because of conflicting
constraints due to noise from the vagaries of human language? To solve this problem, we generate a
graph of all words in the cluster. Each word is a node and arcs are defined by the co-occurrence of two
words in the same phrase. We partition the graph into (possibly overlapping) sub-graphs by searching for
maximal cliques. Intuitively, each sub-graph represents a maximal subset of words and phrases for which
an optimal solution exists. The size of the maximal clique sets the number of attributes |D|. The sub-
graph (words J and phrases I) define the optimization.
jd
5

More formally, we assume that phrases and words are preprocessed and normalized into words as
before. A graph G = (V,E) is a pair of the set of vertices V and the set of edges E. An edge in E is a
connection between two vertices and may be represented as a pair (vi,vj) � V. Each phrase (word)
represents a vertex v in the graph; edges are defined by phrase pairs within a review (word pairs within a
phrase). An N-partite graph is a connected graph where there are no edges in any set of vertices Vi. A
clique of size N simulates a plays the role of arelational schema and can be extended to an N-partite
graph by substituting each vertice vi of the clique with a set of vertices Vi. A database table with disjoint
columns thus represents an N-partite graph where the size of the clique defines the number of columns
and each word in the clique “names” a column. A maximal-complete-N-partite graph is a complete-N-
partite graph not contained in any other such graph; in other words, the initial clique is maximal. The
corresponding database table of phrases represents the existing product attribute space, and the maximal-
complete-N-partite graph includes possibly novel combinations of previously unpaired attributes and/or
attribute properties.
To relate the graph back to customer reviews, we say that a product attribute is constructed from k
dimensions. Each dimension names a domain (D). Each domain D is defined by a finite set of words that
includes the value NULL for review phrases where customers fail to mention one or more attribute
dimension(s). The Cartesian product of domains D1 …Dk is the set of all k-tuples {t1…tk | ti � Di}. Each
phrase is simply one such k-tuple and the set of all phrases in the cluster simply defines a finite subset of
the Cartesian product. A relational schema is simply a mapping of attribute properties A1 …Ak to domains
D1 … Dk. Note the strong, implicit assumption that a maximal clique, taken over a word graph, is a proxy
for the proper number of attribute dimensions. Under this assumption, it is easy to see how searching for
cliques within the graph results in a table.
6

[6] Constrained Logic Programming
To align words into their corresponding attribute dimensions, we frame the task as a
mathematical assignment problem and resolve the problem using a bounds consistency approach. We
define the assignment using the maximal clique that corresponds to the schema for each product attribute
table (see Figure WA1.1). In the bounds consistency approach, we invert the constraints (tok_exclusion)
to express the complementary set of candidate assignments (tok_candidates) for each attribute dimension.
If the phrase constraints, taken together, are internally consistent, then the candidate assignments
(tok_assign)for a given token are simply the intersection of all candidate assignments as defined by all
phrases in the cluster containing that token.
We transform the mutual exclusivity constraint represented by each phrase into a set of candidate
assignments using the algorithm in Figure WA1.2. Note that we need only propagate the mutual
exclusivity of words that are previously unassigned. Accordingly, for each unassigned token in a given
phrase, the set of candidate assignments is the intersection of the possible assignments based upon the
current phrase and all candidate assignments from earlier phrases containing the same token. We
maintain a list of active tokens boundary_list to avoid rescanning the set of all tokens every time the
possible assignments for a given token is updated.
Finally, the K-means clustering used to separate review phrases into distinct product attributes is
a noisy process. The clustering can easily result in the inclusion of spurious phrases. Both the initial
process_phrases(p_list)
[1] schema = find_maximal_clique(p_list)
[2] order phrases by length
[3] for each phrase p:
[4] # initialize data structures
[5] tok_exclusion – for each tok, mutually exclusive tokens
[6] tok_candidates – for each tok, valid candidate assignments
[7] tok_assign – for each tok, the dimension assignment
[8] # propagate the constraints for each successive phrase
[9] tok_candidates, tok_exclusion, tok_assign =
[10] propagate_bounds(phrase, tok_candidates,
[11] tok_exclusion, tok_assign, schema)
[12]
Figure WA1.1 Logical Assignment
7

propagate_bounds(phrase, tok_candidates, tok_exclusion, tok_assign, schema)
[1] # marshall prior assignments
[2] unassigned_tok = {t|t�phrase � t�assign_d}
[3] unassigned_attr = {a|a�schema � �t(t�phrase � a�tok_assign[t])}
[4] for each t in unassigned_tok:
[5] tok_exclusion[t] = (t � (unassigned_tok – t))⋃ tok_exclusion[t]
[6] possible_assign = {a|a�(unassigned_attr ⋂ tok_candidates[t])}
[7] boundary_list = {(t,[possible_assign])} ⋃ boundary_list
[8] recurse_boundary(boundary_list, tok_exclusion, tok_assign)
Figure WA1.2 Propagate boundary constraints
clustering of phrases into product attributes and the subsequent assignment of words to attribute
properties are inherently imperfect. Inconsistencies may emerge for any number of reasons including:
Poor parsing, the legitimate appearance of one word multiple times within a single phrase (e.g. the phrase
‘digital zoom and optical zoom’ duplicates the word ‘zoom’) or even “inaccuracies” by the human
reviewers who write the text that is being automatically processed. This could result in a single attribute
property divided over multiple table columns. For example, some reviews might write "SmartMedia" as a
single word and others might use "Smart" and "media" as two separate words. Alternatively, multiple
product attributes may appear in the same cluster. '[C]ompact flash' and 'compact camera' are clustered
together based upon their common use of the word 'compact,' yet refer to distinct attributes.
To address the problem of robustness in the face of noisy clusters that include references to additional
product attributes or have different properties for the same attributes, we extend our CLP approach to
simultaneously cluster phrases and assign words. By modeling reviews as a graph of phrases, we can
apply the same CLP in a pre-assignment step to filter a single (noisy) cluster of phrases. As alluded to in
Appendix B.2, we generate a graph where phrases are nodes, and edges represent the co-occurrence of
two phrases within the same review. The extended CLP then prunes phrases by recursively applying co-
occurrence constraints; two phrases in the same review cannot describe the same attribute just as two
words in the same phrase cannot describe the same attribute dimension. The same assignment
representation removes phrases that are not central to the product attribute at the heart of a particular
8

phrase cluster. Phrases that are not “connected” in the graphical sense of a connected component or
represent conflicting constraints are simply excluded from the subcluster.
Unfortunately, even the extended CLP approach is imperfect. Some of the tables will represent
distinct product attributes. Others will simply constitute random noise. Individual tables are supposed to
represent distinct product attributes, so we assume that meaningful tables should contain minimal word
overlap. With this in mind, we apply a two-stage statistical filter to further filter noisy clusters.
First, because each table itself separates tokens into attribute properties (columns), meaningful
tables will not hold too small a percentage of the overall number of tokens. Second, we assume that
meaningful tables comprise a (predominately) disjoint token subset. If the tokens in a table appear in no
other table, then the intra-table token frequency should match the frequency of the initial k-means cluster;
likewise, the table's tokens, when ordered by frequency, should match the relative frequency-based order
of the same tokens within the initial cluster. The first stage of our statistical filter is evaluation of a � 2
statistic, comparing each table to its corresponding initial cluster. Although there is no hypothesis to be
tested per se, there is a history of applying the � 2 statistic in linguistics research to compare different sets
of text with a measure that weights higher-frequency tokens with greater significance than lower
frequency tokens (Kilgarriff 2001). In our case, we set a minimum threshold on the � 2 statistic to ensure
that individual tables reflect an appropriate percentage of tokens from the initial cluster.
After filtering out tables that do not satisfy the � 2 threshold, we use the same cluster token counts
to calculate rank order statistics. We compare the token rank order from each constituent table to that in
the corresponding initial cluster using a modified Spearman rank correlation co-efficient (rs). As a minor
extension, we use the relative token rank, meaning that we maintain order but keep only tokens that are in
both the initial and the iterated CLP cluster(s). We select as significant those tables that maximize rs. In
the event that two or more tables maximize rs we promote all such subclusters either as a noisy cluster or
as synonymous words for the same product attribute as determined by a manual reading.
9

Web Appendix B. Automatically generated attributes and their corresponding properties and levels
1. Picture (what/where)
tough picture
wildlife, watersports, vivid, underexposure, unclear, unattractive, took, surprisingly, sun, streaky, crisp, sharpest, color
regret, recommend, quality, printable, proof, outside, noisy, minimum, maximum, lost, kid, immediately, hit,
guess, fully, frame, far, excellent, even, definitely, daytime, counter, construct, base, adequate, absolutely
vibrant underexpose
plenty, gallery
consist
stong, inaccuracy, blotchy
2. Flash (memory, card, photo)
1 flash type card
slot 2 immediate
meg bigger
use cheap
compact fragile
hard
3. Slow (start-up, turn on, recovery)
shutter reaction, slow, fast, mode, set, control, active, auto
exposure long, no, high quality, wide, action
us speed automatic bit top
recovery lag, second feature little
wonder sluggish turbo button, operate time, adjust, manual, take, range
1 need release delay
virtual shot hard 2, response press
4. Resolution
capacity megapixel low effect
resolution cost really high
lcd 1.5
5. Megapixel
mpix 4.0 access life
megapixel 5 set
pixel re mega
pixl improve meg
stick 6.3 map
4.10, 4.1, 3.4, 2.6, 2.3, 2.0, 11, 1.1, .8, 0 come
6. Lens (cap, quality, manufacturer)
easily cap pro lost lens quality
leash avail variable no leica
telephoto option average damage amazing
loose nikon
attach famous, distance
canon
10

7. Optical (zoom, viewfinder)
low optical viewfinder chintzy zoom digital
blurry wide telephoto
16 variance lens
x3 option
x10 correction
smallest, power, bigger, 7, large, lack,
feature, decent, cheat, available, 2.8, 2.5x
8. Body (design, construction)
body feel fragile plastic
look flimsy ugly
built small nice
compact solid metal
camera fairly indo
9. Print (size, quality, output)
print photo hard quality average
produce beautiful match image
film 4x6 high
perfect copies
adjust, accuracy, misleading,
inaccurate
10. Zoom
capable clear, digit, lack, fuzzy, limit, efficient zoom 2.5
definitely incredible lens useless
us, nice, fast loose tricky stink
non feature no seamless
little, function, somewhat benefit 2.5x 1.6x, distance, crap, 32
true, really 3 lack, fuzziness, limit, efficiently 12
11. Feel (mfr, construction)
solid, camera look, old little design
film feel point nice
thing, simpler want, supp, resembles, bulkier, hard right build
35 way, finish kind, somewhat us
cheap, plentiful built
starter, get extra, faulty awesome hold
12. Menu
control, menu versus us control option slow
tough relative screen read manual, ,
cumbersome, bury,
plain, inexpensive
quirky, min sensitive
easy manual up set, multiple, lot, difficult simple, complicated, scatter
full, extensive,
lack, no
hidden, awkward
custom, exposure preview option hidden function, scheme navigate petroglyph
complicated
decent familiar feature nice, layout, larger,
imbed, hopeless,
clear, camera, maze,
ergonomic, annoy
compress minimum interface, graphic design
13. Shoot
shoot adjust mode fast camera
stitch slew, rapid, preset, multiple, lot, lack, numerous sound digital repetitive
movie microphone point
function clip, need look
option infinitely just
11

14. Support (service)
support product terrible custom
service tech inadequate
need
indifferent
organize
quick
15. LCD
see light screen hard lcd bright unimpressive, twist, stripe, dull, sharper, lackluster, innovative,
deceptively, placement, location, flexible, clarity, accurate, 1.5
sunlight soft difficult rotate
night panel dim unprotected, articulated, huge, clear
display, automatic outside little
pretty
review exposure
daylight
relatively
16. Movie (audio, visual)
control sound set unlimited length clip video
second 30 take price length
movie quicktime no avi
lot audio vga mpeg mode
quality capture useless 60
capability feature
17. USB
platform, computer upload easy, weird given speed
dock transfer file use connect
usb, convenient charge brainless, flawless picture
sound video, come, station mean
archive large option pc
cable travel slow camera
serial download quick tv
av link problem
18. Focus
focus auto fast
soft
nice
19. Software
interface benefit image no, slideshow,
include, bundle,
download, view,
easyshare, zoom, browse
useful, capable,
improve,
tv, external, provided,
bundled, use, tedious,
compatible, slightly, twain
easy
method, easily,
custom
software
computer clunky computer, user friendly, average possible
proprietary complicated special window retain
fun manage clumsy tricky, function contrast
mac, somewhat, 5 set weak, wonder, capture, suck, include, basic, hookup
option, little, package, kodak decent, lack,
creative
low, install, say, reload program
difficult xp
12
output, interface, box, way,
pretty, extremely,
particularly, generous

20. Cover (lens, LCD, battery)
lcd cover screen no close step, interfere, care
camera protect display slide tricky
strap, built, auto, automatic lenses side fully zoom
integrated lens open
rubber afterthought lock
retract batteries flimsy mechanical
21. Price
price place little higher, nice, reason
long easy, ease point, fair use
speed autofocus range time
fast perfect super high
cheap startup
cost sluggish
22. Memory/Screen
need memory large lcd
fairly screen travel view, review,
use window additional protect
proprietary panel sony dirty
money upgrade, removable
built
23. Floppy (storage media)
floppy access disk versatile easy
use no
storage
operate
cheap
24. Disk
disc computer use, regular
easier disk load floppy
media easi transfer 1.44mb
diskette medium, space removable
storage limited, choice
cheap
25. Battery (life, use, type)
sure pretty, little, aa,
eats, run, 4,
power, ion,
supply, built,
take, back
tomorrow
recharge, extra,
infolithium
up use preview batteries
lot, quick, nimh, 2,
pack, charger, right,
wonder
included,
inexpensive, keep,
drain, suck
fast, quickyi, alkaline,
really, regular, lithium, ,
hour, no, need
13
battery
life, time display thing cruddy just give
ease remove, camera price weigh because socket
bring expensive long last easy liion
terrible real low charge lose
system

26. Size
small, broke, convenient, durable,
intuitive, mechanical , near, big,
problem, pleasant, case, bigger,
smaller, larger, large, versatile,
cumbersome, money, status, heavy,
guide, nice, no, clearer, easily, dim
design, tad, weight, slightly, kinda, lot,
frustrating expensive, icon, capable,
incredible, extremely, stick, chassis,
function, mirror, possible, perfect, little,
right, require, hard, cheap, somewhat, bit,
2.2, digit, pretty, compare, damage,
pocket, flip
non, awkward a101 stuffed
couple time finepix
true, nearly, jean wish drop
expect unusual, separate software
crack unit, shape, moveable preset
14
sleek, lightweight, cam, , size, display,
quirk, button, turn, color, complicated,
ton, fairly, pro, ergonomic, solid, bulky,
ergo, rugged, viewfinder, basic, highly,
carry, pressure
27. Photo quality
photo loose
unfocused wash
min trait
touch, suburb, white, tint, suction, stun, stitch, soft, share, landscape, rupture, retouch, result, realist, quality, pretty,
plan, noisy, move, manage, lost, length, generous, fuzzy, fully, file, fabulous, eras, hard, amicable, alter, actual,
accurate, 250, floppy, downtime
unexpected
0
28. Low light
auto lowlight little focus low difficult, option bit reliable
dim light touchy set certain mix situation capable
object hunt, finicky conditions auto, lot question, night grainy
use lamp blind assist judge harder accurate
aid hard, long,
inability,
difficult
annoy,
awkward,
inadequate,
laser
fast, take, average,
assist, slightly, range,
system, level
adjustable no, problem room condition, trouble,
iffy, motion, way,
lag, need, set,
relatively, occasion
imperfect
especially, limit,
sensitive, focus, dark,
dismal, fare, cost,
noisy, weak, finicky
29. Control
control obvious, parallax
dist, underexposure, solid, ergonomic, unintuitive, tiny, simpler, sensitive, place, pad, odd, finicky, familiar, dummy,
overexposure
color, clear, basic, analog, clumsy, individual
stigma casio
frame, refund, size slight
slightly full
guess, time, big
30. Macro (lens)
macro design, zero, unimpressed, built, average, lack, awesome mode
no, amazing, fussy, real, function
super, great. fantastic, poor stupendous
nice possible, ability
incredible feature
closeup, unsurpassed

31. MB (memory)
mb media, picture included 8 smart card
flash small lowly quality memory
no usb 4, 32 16 flashcard
compactflash 2, 7, 11, provided stick, take onboard, internal, run, wimpy, pricey
come, skimpy little 128 way smartcard
need avail size measly recommended
32. Edit (in camera)
edit software capable package
effect onboard image limited
no
average
33. Red eye
problem flash eye red hue
built massive indoors low background
pic occasion poor yellow look,
anti lot catch light
show tint
unbearable, frequent, easier, deflect, appear, eliminate, control
34. Shutter (delay, lag)
shutter lag turbo button, operate delay
us speed automat bit top
recovery sluggish second feature little
wonderful reaction, slow, fast, long, no, mode, set, control, active, quality, wide, time, adjust, manual, take,
exposure high,
action
rang
virtual 1 need release auto
shot hard 2, responsive press
35. Features
feature manual, newer, g2, extend, , switch, change, readily lot, additional, 6, need, avail, expert, document, lack
place practice, neat, cool, extra range, array
point wide, incredible, hard, access easy, ton, hard, difficult
small, large, huge, sell, limit, impress, basic set, want, readable
pro semi, level
long, competitive list
36. Instructions
instruction clear
unique from
lot
lose
lack
answer
37. Adapter (AC)
separate ac purchase no available adapter
need Level 1: included optional power external
50 comes case
charger
usb
15

38. Picture quality
extremely, switch poor picture quality
surprisingly, printable, mediocre, amazingly image cap
print design
zoom distance
durable
lens
39. Image (quality)
inconsistent image quality
webcam, super, profession, margin, hard, over, ok, nikon, mediocre, lcd, indifferent, wonder, unacceptable,
satisfactory, terrible, problem, overprocessed, nice, mean, fair, expect, class, awful, average, astounding,
astonishing, accept, medium, addicted, horrible, generally, control, case, before
sharp
incredible need
color
produce
detail
color
16

Web Appendix C. User Survey
In this Appendix, we list the digital camera product attributes (with duplicates eliminated) that are
found exclusively in one or more online buying guides (Expert Only), learned automatically from the
product reviews (VOC Only), or in both (Expert + VOC). The means for Familiarity and Importance as
collected from our survey are reported on a 1 to 7 scale. To help align the attribute names used here with
those in Table 2, we include a mapping from the automatically derived attribute clusters (auto) to those 55
attributes used in the consumer survey. Note that in some cases, an automatically derived attribute is
mapped to more than one survey (expert) attribute name and vice versa due to inconsistencies between the
granularity with which an attribute is discussed in the expert guides and/or by the Voice of the Consumer.
Expert Only
Survey attribute Familiarity Importance
battery source 6.42 6.05
flash ext 4.91 3.09
flash range 4.17 3.90
image compress 4.80 4.62
image sensor 2.37 3.25
image stab 4.88 5.28
man light sens 3.69 3.69
manual exp 2.69 3.10
manual light meter 2.38 2.86
manual shut 3.09 3.24
mem qty built-in 5.68 5.05
movie fps 4.43 4.35
movie output 3.90 4.20
music play 4.57 3.80
num sensors 2.25 3.18
power adapt 6.05 4.87
time lapse 4.45 3.76
wide angle 3.57 3.57
17

VOC Only
Survey attribute Auto Familiarity Importance
camera size size 6.35 5.87
body (design) body 5.82 5.48
download time USB 5.68 4.75
feel (durability) feel; support service 5.30 5.43
instructions instruction 6.07 4.18
lcd brightness screen 4.62 4.57
shutter lag slow; shutter 3.87 3.80
twist lcd cover 3.80 3.62
Expert + VOC
Survey attribute Auto Familiarity Importance
battery life battery 6.50 6.30
cam soft edit 5.32 3.57
cam type shoot 4.65 5.06
comp cxn USB 6.13 5.75
comp soft software 5.77 4.60
ergonomic feel feel 5.15 4.83
flash built-in red-eye 6.45 6.26
flash mode low-light; red-eye 5.48 5.12
lcd viewfinder lcd 5.00 5.15
lens cap cover; lens 4.92 4.25
lens type macro 3.80 4.20
manual aper control 2.40 2.86
manual focus control; focus 5.14 3.72
mem capacity mb 5.30 5.68
mem stor type disk; floppy; flash (drive) 5.63 5.47
movie audio movie 4.85 4.48
movie length movie 5.73 5.47
movie res mpeg 4.97 5.15
navigation menu 5.93 5.40
optical viewfinder optical
photo qual; picture; print;
4.50 4.33
picture quality
image qual 6.35 6.60
price price 6.31 6.45
resolution resolution; megapixel 5.35 5.56
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shot modes features 5.55 4.67
shutter delay slow; shutter 4.27 3.95
shutter speed control 4.81 4.60
white bal features 3.40 3.74
zoom dig zoom 5.07 4.47
zoom opt zoom; optical 5.52 5.21
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Web Appendix D. Interpreting the Correspondence Analysis dimensions.
Correspondence Analysis is an approach to dimension reduction when analyzing high-
dimensional, two-mode, two-way count data (Everitt and Dunn 2001). To interpret the dimensions in the
reduced space, we regressed each brand’s factor scores on the derived attributes. For each figure in the
paper, we report the results of the stepwise regression on F1 and F2. The reported results assume a
probability for entry of .05 and a probability of removal of .1.
To make the dimensions both interpretable and actionable, we follow the marketing literature in
relating customer needs, as represented by user generated reviews, to actionable manufacturer
specifications, as in the "House of Quality" (Hauser and Clausing 1988). Specifically, product attributes
elicited from online reviews include different granularities ("zoom" versus "10x optical zoom") or reflect
different levels of technical sophistication ("low-light settings" vs. "iso settings"). Consulting the set of
professional buying guides, we manually mapped our 39 automatically generated attributes onto a coarser
but actionable categorization of specifications. The visualizations are generated and interpreted using
these meta-attributes.
lens cap/cover resolution manual focus lens rechargeable battery image resolution/print qual
feel durability price movie/video zoom image stabil manual controls
start-up time pc connect navigation weight software picture types
shutter-lag flash bult - in storage type size low-light/light control service/warranty
shot-delay camera type white balance lcd autofocus
Table WD4.1 Actionable meta-attributes for regressing explaining CA dimensions
Figure 3. Mapping the market using customer reviews
Summary of attribute selection for dimension F1.
No. of
Adjusted
variables Attribute MSE R² R²
1 navigation .000 .897 .882
2 storage type .000 .992 .989
3 movie/video .000 1.000 .999
Summary of attribute selection for dimension F2.
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No. of
Adjusted
variables Attribute MSE R² R²
1 low light .000 .913 .900
2 lens .000 .988 .983
3 price .000 1.000 .999
Figure 5. Market structure by Cons with Pros as supplementary points
Summary of attribute selection for dimension F1.
No. of
Adjusted
variables Attribute MSE R² R²
1 image resolution/print qual .000 .929 .918
2 start-up time .000 .972 .963
3 lens/cap cover .000 .989 .982
4 shutter-lag .000 .996 .992
5 service-warranty .000 1.000 .999
Summary of attribute selection for dimension F2.
No. of
Adjusted
variables Attribute MSE R² R²
1 lens .001 .689 .644
2 navigation .000 .933 .911
3 size .000 .973 .956
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