Amazon QA — Automotive

A Amazon dataset of 89,923 QA pairs in Automotive, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Automotive. Suitable for question answering, sentiment analysis, product understanding, conversational AI.

#Automotive#Mcauley#Product question answering#Question answering

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Analytics

Content metrics

89,923 records scored · min–maxmean ± 0.675 σ (the interquartile range if the values are roughly normal)mean

unixTimeμ 1.3B ± 304.7M
min 0max 1.4B
has answer100.0% true
true 100.0%false 0.0%
is yes no56.6% true
true 56.6%false 43.4%
is open ended43.4% true
true 43.4%false 56.6%
5.3
Flesch–Kincaid grade
US school grade level
5.1
Automated readability index
Frequent terms
fitworkyesuseneedcarsuredonknowbatterylikegoodsizeproductcomethinklongvehiclefitsgreatmodelreartruckfordwantworkshondacoverenginerightamazonbuythankskitboughtsetchecktoyotahopetimeplugnewtireairunitpoweroillighthelpsdifferent
Topic clusters

Topic 0

battery
wire
charge
plug
light
power
bulb
charger

Topic 1

wax
paint
use
clean
car
product
dry
remove

Topic 2

fit
vehicle
amazon
model
yes
car
number
website

Topic 3

mount
mirror
bolt
hole
hitch
bracket
screw
plate

Topic 4

oil
filter
hose
pump
fuel
engine
use
valve

Topic 5

dodge
truck
ram
bed
fit
cab
spring
tailgate

Topic 6

tire
wheel
trailer
size
chain
lug
fit
cover

Topic 7

fit
chevy
toyota
nissan
work
subaru
mazda
camaro

Topic 8

ford
fit
ranger
explorer
mustang
buick
xlt
work

Topic 9

sensor
upstream
downstream
bank
denso
oxygen
converter
code

Topic 10

yes
galaxie
magnum
colorado
atf
lip
compartment
female

Topic 11

dimension
inch
size
width
length
wide
long
height
Show all 13 topics

Topic 12

honda
accord
civic
fit
acura
coupe
engine
odyssey
Top hashtags
#6553227
#124
#215
#10868
#67
#100036
#10885
#10905
#125
#35
#45
#75
#104
#10724
#10774
#10844
#10854
#10874
#144
#84

Sampled: text metrics from 50K of 179.8K records; topic model from 20K.

Record

Catalog key
amazon_qa_Automotive
Authors
Wan, Mengting; McAuley, Julian
Year
2016
Source paper
Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems
DOI / arXiv
10.1109/ICDM.2016.0060
License
No licence stated by the authors (citation requested)
Records
89,923
Languages
en
Collected
2013-2015 (2010s)
Region
Global (inferred: the platform is used worldwide)

Provenance

Originally published at
https://cseweb.ucsd.edu/~jmcauley/datasets.html#amazon_qa link checked
Hosted copy
Mirrored on echo.directory on 2026-10-02, 8.2 MB across 1 files. Checksums in MANIFEST.json.

How to cite

BibTeX

@misc{echo2026,
  title  = {ECHO: Exploratory Compendium of Humanity Online},
  author = {Switzer, Braden and Zafarani, Reza},
  year   = {2026},
  url    = {https://echo.directory},
  note   = {Syracuse University}
}

@inproceedings{Wan_2016,
  title     = {Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems},
  url       = {http://dx.doi.org/10.1109/ICDM.2016.0060},
  doi       = {10.1109/icdm.2016.0060},
  booktitle = {2016 IEEE 16th International Conference on Data Mining (ICDM)},
  publisher = {IEEE},
  author    = {Wan, Mengting and McAuley, Julian},
  year      = {2016},
  month     = Dec,
  pages     = {489–498}
}

APA

Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory

Wan, M., & McAuley, J. (2016). Amazon QA — Automotive [Data set]. https://doi.org/10.1109/ICDM.2016.0060

MLA

Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.

Wan, Mengting, and Julian McAuley. Amazon QA — Automotive. 2016, doi.org/10.1109/ICDM.2016.0060.

BibTeX · RIS · CSL-JSON

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