Sentence-Based Text Analysis for Customer Reviews
Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured...
Gespeichert in:
Veröffentlicht in: | Marketing science (Providence, R.I.) R.I.), 2016-11, Vol.35 (6), p.953-975 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 975 |
---|---|
container_issue | 6 |
container_start_page | 953 |
container_title | Marketing science (Providence, R.I.) |
container_volume | 35 |
creator | Büschken, Joachim Allenby, Greg M. |
description | Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from
www.expedia.com
and
www.we8there.com
. Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis.
Data, as supplemental material, are available at
https://doi.org/10.1287/mksc.2016.0993
. |
doi_str_mv | 10.1287/mksc.2016.0993 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_gale_infotracgeneralonefile_A473845207</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A473845207</galeid><jstor_id>45158464</jstor_id><sourcerecordid>A473845207</sourcerecordid><originalsourceid>FETCH-LOGICAL-c671t-29249a01d2b8ecfcfe58a587f271994e5004fb5a2b32ceaaef37be8ed19174903</originalsourceid><addsrcrecordid>eNqFkt1rFDEUxQex4Fp99XlAFB-cNZ-T5HG7tCoUBK3gW8hkb9as81FzZ2z73zfDCu3KggQSCL9zbu7NKYpXlCwp0-pD9wv9khFaL4kx_EmxoJLVlRT6x9NiQRRnFePGPCueI-4IIYoRvSjoN-hH6D1UZw5hU17B7ViuetfeYcQyDKlcTzgOHaTyK_yJcIMvipPgWoSXf8_T4vvF-dX6U3X55ePn9eqy8rWiY8UME8YRumGNBh98AKmd1CowRY0RIAkRoZGONZx5cA4CVw1o2FBDlTCEnxbv9r7Xafg9AY62i-ihbV0Pw4SW6rquhdK1yejrf9DdMKXcxEzlQlpwqR6orWvBxj4MY3J-NrUrobgWkpGZqo5QW-ghuXboIcR8fcAvj_B5baCL_qjg7YEgM2Me-tZNiPYQfP8IbCaMPWDeMG5_jrjnjz3EpwExQbDXKXYu3VlK7BwQOwfEzgGxc0Cy4M1esMsfnB7TjBNlhaRSi1o8TGRuLnX4P997iuLEXw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1850084357</pqid></control><display><type>article</type><title>Sentence-Based Text Analysis for Customer Reviews</title><source>INFORMS PubsOnLine</source><source>Business Source Complete</source><source>JSTOR Archive Collection A-Z Listing</source><creator>Büschken, Joachim ; Allenby, Greg M.</creator><creatorcontrib>Büschken, Joachim ; Allenby, Greg M.</creatorcontrib><description>Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from
www.expedia.com
and
www.we8there.com
. Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis.
Data, as supplemental material, are available at
https://doi.org/10.1287/mksc.2016.0993
.</description><identifier>ISSN: 0732-2399</identifier><identifier>EISSN: 1526-548X</identifier><identifier>DOI: 10.1287/mksc.2016.0993</identifier><identifier>CODEN: MARSE5</identifier><language>eng</language><publisher>Linthicum: INFORMS</publisher><subject>Analysis ; Bayesian analysis ; big data ; Consumer behavior ; Customer feedback ; extended LDA model ; Marketing research ; Product reviews ; Studies ; Surveys ; Syntax ; Text analysis ; text data ; unstructured data ; user-generated content</subject><ispartof>Marketing science (Providence, R.I.), 2016-11, Vol.35 (6), p.953-975</ispartof><rights>2016 INFORMS</rights><rights>COPYRIGHT 2016 Institute for Operations Research and the Management Sciences</rights><rights>Copyright Institute for Operations Research and the Management Sciences Nov-Dec 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c671t-29249a01d2b8ecfcfe58a587f271994e5004fb5a2b32ceaaef37be8ed19174903</citedby><cites>FETCH-LOGICAL-c671t-29249a01d2b8ecfcfe58a587f271994e5004fb5a2b32ceaaef37be8ed19174903</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/45158464$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://pubsonline.informs.org/doi/full/10.1287/mksc.2016.0993$$EHTML$$P50$$Ginforms$$H</linktohtml><link.rule.ids>314,780,784,803,3692,27924,27925,58017,58250,62616</link.rule.ids></links><search><creatorcontrib>Büschken, Joachim</creatorcontrib><creatorcontrib>Allenby, Greg M.</creatorcontrib><title>Sentence-Based Text Analysis for Customer Reviews</title><title>Marketing science (Providence, R.I.)</title><description>Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from
www.expedia.com
and
www.we8there.com
. Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis.
Data, as supplemental material, are available at
https://doi.org/10.1287/mksc.2016.0993
.</description><subject>Analysis</subject><subject>Bayesian analysis</subject><subject>big data</subject><subject>Consumer behavior</subject><subject>Customer feedback</subject><subject>extended LDA model</subject><subject>Marketing research</subject><subject>Product reviews</subject><subject>Studies</subject><subject>Surveys</subject><subject>Syntax</subject><subject>Text analysis</subject><subject>text data</subject><subject>unstructured data</subject><subject>user-generated content</subject><issn>0732-2399</issn><issn>1526-548X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><recordid>eNqFkt1rFDEUxQex4Fp99XlAFB-cNZ-T5HG7tCoUBK3gW8hkb9as81FzZ2z73zfDCu3KggQSCL9zbu7NKYpXlCwp0-pD9wv9khFaL4kx_EmxoJLVlRT6x9NiQRRnFePGPCueI-4IIYoRvSjoN-hH6D1UZw5hU17B7ViuetfeYcQyDKlcTzgOHaTyK_yJcIMvipPgWoSXf8_T4vvF-dX6U3X55ePn9eqy8rWiY8UME8YRumGNBh98AKmd1CowRY0RIAkRoZGONZx5cA4CVw1o2FBDlTCEnxbv9r7Xafg9AY62i-ihbV0Pw4SW6rquhdK1yejrf9DdMKXcxEzlQlpwqR6orWvBxj4MY3J-NrUrobgWkpGZqo5QW-ghuXboIcR8fcAvj_B5baCL_qjg7YEgM2Me-tZNiPYQfP8IbCaMPWDeMG5_jrjnjz3EpwExQbDXKXYu3VlK7BwQOwfEzgGxc0Cy4M1esMsfnB7TjBNlhaRSi1o8TGRuLnX4P997iuLEXw</recordid><startdate>20161101</startdate><enddate>20161101</enddate><creator>Büschken, Joachim</creator><creator>Allenby, Greg M.</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>XI7</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20161101</creationdate><title>Sentence-Based Text Analysis for Customer Reviews</title><author>Büschken, Joachim ; Allenby, Greg M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c671t-29249a01d2b8ecfcfe58a587f271994e5004fb5a2b32ceaaef37be8ed19174903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Analysis</topic><topic>Bayesian analysis</topic><topic>big data</topic><topic>Consumer behavior</topic><topic>Customer feedback</topic><topic>extended LDA model</topic><topic>Marketing research</topic><topic>Product reviews</topic><topic>Studies</topic><topic>Surveys</topic><topic>Syntax</topic><topic>Text analysis</topic><topic>text data</topic><topic>unstructured data</topic><topic>user-generated content</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Büschken, Joachim</creatorcontrib><creatorcontrib>Allenby, Greg M.</creatorcontrib><collection>CrossRef</collection><collection>Gale Business: Insights</collection><collection>Business Insights: Essentials</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Marketing science (Providence, R.I.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Büschken, Joachim</au><au>Allenby, Greg M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sentence-Based Text Analysis for Customer Reviews</atitle><jtitle>Marketing science (Providence, R.I.)</jtitle><date>2016-11-01</date><risdate>2016</risdate><volume>35</volume><issue>6</issue><spage>953</spage><epage>975</epage><pages>953-975</pages><issn>0732-2399</issn><eissn>1526-548X</eissn><coden>MARSE5</coden><abstract>Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from
www.expedia.com
and
www.we8there.com
. Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis.
Data, as supplemental material, are available at
https://doi.org/10.1287/mksc.2016.0993
.</abstract><cop>Linthicum</cop><pub>INFORMS</pub><doi>10.1287/mksc.2016.0993</doi><tpages>23</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0732-2399 |
ispartof | Marketing science (Providence, R.I.), 2016-11, Vol.35 (6), p.953-975 |
issn | 0732-2399 1526-548X |
language | eng |
recordid | cdi_gale_infotracgeneralonefile_A473845207 |
source | INFORMS PubsOnLine; Business Source Complete; JSTOR Archive Collection A-Z Listing |
subjects | Analysis Bayesian analysis big data Consumer behavior Customer feedback extended LDA model Marketing research Product reviews Studies Surveys Syntax Text analysis text data unstructured data user-generated content |
title | Sentence-Based Text Analysis for Customer Reviews |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T05%3A34%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Sentence-Based%20Text%20Analysis%20for%20Customer%20Reviews&rft.jtitle=Marketing%20science%20(Providence,%20R.I.)&rft.au=B%C3%BCschken,%20Joachim&rft.date=2016-11-01&rft.volume=35&rft.issue=6&rft.spage=953&rft.epage=975&rft.pages=953-975&rft.issn=0732-2399&rft.eissn=1526-548X&rft.coden=MARSE5&rft_id=info:doi/10.1287/mksc.2016.0993&rft_dat=%3Cgale_proqu%3EA473845207%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1850084357&rft_id=info:pmid/&rft_galeid=A473845207&rft_jstor_id=45158464&rfr_iscdi=true |