Loading…

Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams

Policy makers and environmental organizations have a keen interest in awareness building and the evolution of stakeholder opinions on environmental issues. Mere polarity detection, as provided by many existing methods, does not suffice to understand the emergence of collective awareness. Methods for...

Full description

Saved in:

Bibliographic Details
Main Authors:	Weichselbraun, Albert, Scharl, Arno, Gindl, Stefan
Format:	Conference Proceeding
Language:	English
Subjects:	Classification climate change Conferences Digital media Earth Evolution Feature extraction keyword analysis Media Meteorology opinion mining opinion target extraction Organizations Platforms Polarity Sentences sentiment analysis Social networks Syntactics YouTube
Online Access:	Request full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by
cites
container_end_page	1048
container_issue
container_start_page	1040
container_title
container_volume
creator	Weichselbraun, Albert Scharl, Arno Gindl, Stefan
description	Policy makers and environmental organizations have a keen interest in awareness building and the evolution of stakeholder opinions on environmental issues. Mere polarity detection, as provided by many existing methods, does not suffice to understand the emergence of collective awareness. Methods for extracting affective knowledge should be able to pinpoint opinion targets within a thread. Opinion target extraction provides a more accurate and fine-grained identification of opinions expressed in online media. This paper compares two different approaches for identifying potential opinion targets and applies them to comments from the YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and measures their ability to handle noisy input data from social media streams.
doi_str_mv	10.1109/HICSS.2016.133
format	conference_proceeding
fullrecord	<record><control><sourceid>proquest_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_7427311</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7427311</ieee_id><sourcerecordid>1816027563</sourcerecordid><originalsourceid>FETCH-LOGICAL-i248t-532ec6b4e62d940d84336575367fc67e4c294aac23abea57fe0ff4b86f7d38743</originalsourceid><addsrcrecordid>eNotj81LwzAchqMouE2vXrzk6KUz32mPUjY3mOzQiceSpr-MSJvOpBv63zuYpwdeHl54EHqkZE4pKV5W67Kq5oxQNaecX6Ep0aqQUmlCr9GESc0ylSt2gyZUcpJRReQdmqb0RQgjgqoJqhY_YzR29GGPtwcf_BDwzsQ9jAm7OPR4EU4-DqGHMJoOf0KDy-EE0ewBm9DiarD-vL9D6w2uxgimT_fo1pkuwcM_Z-hjudiVq2yzfVuXr5vMM5GPmeQMrGoEKNYWgrS54FxJLbnSzioNwrJCGGMZNw0YqR0Q50STK6dbnmvBZ-j58nuIw_cR0lj3PlnoOhNgOKaa5udgpqXiZ_XponoAqA_R9yb-1lowzSnlfy2aXq0</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>1816027563</pqid></control><display><type>conference_proceeding</type><title>Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams</title><source>IEEE Xplore All Conference Series</source><creator>Weichselbraun, Albert ; Scharl, Arno ; Gindl, Stefan</creator><creatorcontrib>Weichselbraun, Albert ; Scharl, Arno ; Gindl, Stefan</creatorcontrib><description>Policy makers and environmental organizations have a keen interest in awareness building and the evolution of stakeholder opinions on environmental issues. Mere polarity detection, as provided by many existing methods, does not suffice to understand the emergence of collective awareness. Methods for extracting affective knowledge should be able to pinpoint opinion targets within a thread. Opinion target extraction provides a more accurate and fine-grained identification of opinions expressed in online media. This paper compares two different approaches for identifying potential opinion targets and applies them to comments from the YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and measures their ability to handle noisy input data from social media streams.</description><identifier>ISSN: 1530-1605</identifier><identifier>EISSN: 2572-6862</identifier><identifier>EISSN: 1530-1605</identifier><identifier>EISBN: 0769556701</identifier><identifier>EISBN: 9780769556703</identifier><identifier>DOI: 10.1109/HICSS.2016.133</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Classification ; climate change ; Conferences ; Digital media ; Earth ; Evolution ; Feature extraction ; keyword analysis ; Media ; Meteorology ; opinion mining ; opinion target extraction ; Organizations ; Platforms ; Polarity ; Sentences ; sentiment analysis ; Social networks ; Syntactics ; YouTube</subject><ispartof>2016 49th Hawaii International Conference on System Sciences (HICSS), 2016, p.1040-1048</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7427311$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,23908,23909,25117,27900,27901,54529,54906</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7427311$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Weichselbraun, Albert</creatorcontrib><creatorcontrib>Scharl, Arno</creatorcontrib><creatorcontrib>Gindl, Stefan</creatorcontrib><title>Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams</title><title>2016 49th Hawaii International Conference on System Sciences (HICSS)</title><addtitle>HICSS</addtitle><description>Policy makers and environmental organizations have a keen interest in awareness building and the evolution of stakeholder opinions on environmental issues. Mere polarity detection, as provided by many existing methods, does not suffice to understand the emergence of collective awareness. Methods for extracting affective knowledge should be able to pinpoint opinion targets within a thread. Opinion target extraction provides a more accurate and fine-grained identification of opinions expressed in online media. This paper compares two different approaches for identifying potential opinion targets and applies them to comments from the YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and measures their ability to handle noisy input data from social media streams.</description><subject>Classification</subject><subject>climate change</subject><subject>Conferences</subject><subject>Digital media</subject><subject>Earth</subject><subject>Evolution</subject><subject>Feature extraction</subject><subject>keyword analysis</subject><subject>Media</subject><subject>Meteorology</subject><subject>opinion mining</subject><subject>opinion target extraction</subject><subject>Organizations</subject><subject>Platforms</subject><subject>Polarity</subject><subject>Sentences</subject><subject>sentiment analysis</subject><subject>Social networks</subject><subject>Syntactics</subject><subject>YouTube</subject><issn>1530-1605</issn><issn>2572-6862</issn><issn>1530-1605</issn><isbn>0769556701</isbn><isbn>9780769556703</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2016</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81LwzAchqMouE2vXrzk6KUz32mPUjY3mOzQiceSpr-MSJvOpBv63zuYpwdeHl54EHqkZE4pKV5W67Kq5oxQNaecX6Ep0aqQUmlCr9GESc0ylSt2gyZUcpJRReQdmqb0RQgjgqoJqhY_YzR29GGPtwcf_BDwzsQ9jAm7OPR4EU4-DqGHMJoOf0KDy-EE0ewBm9DiarD-vL9D6w2uxgimT_fo1pkuwcM_Z-hjudiVq2yzfVuXr5vMM5GPmeQMrGoEKNYWgrS54FxJLbnSzioNwrJCGGMZNw0YqR0Q50STK6dbnmvBZ-j58nuIw_cR0lj3PlnoOhNgOKaa5udgpqXiZ_XponoAqA_R9yb-1lowzSnlfy2aXq0</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Weichselbraun, Albert</creator><creator>Scharl, Arno</creator><creator>Gindl, Stefan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20160101</creationdate><title>Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams</title><author>Weichselbraun, Albert ; Scharl, Arno ; Gindl, Stefan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i248t-532ec6b4e62d940d84336575367fc67e4c294aac23abea57fe0ff4b86f7d38743</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Classification</topic><topic>climate change</topic><topic>Conferences</topic><topic>Digital media</topic><topic>Earth</topic><topic>Evolution</topic><topic>Feature extraction</topic><topic>keyword analysis</topic><topic>Media</topic><topic>Meteorology</topic><topic>opinion mining</topic><topic>opinion target extraction</topic><topic>Organizations</topic><topic>Platforms</topic><topic>Polarity</topic><topic>Sentences</topic><topic>sentiment analysis</topic><topic>Social networks</topic><topic>Syntactics</topic><topic>YouTube</topic><toplevel>online_resources</toplevel><creatorcontrib>Weichselbraun, Albert</creatorcontrib><creatorcontrib>Scharl, Arno</creatorcontrib><creatorcontrib>Gindl, Stefan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Weichselbraun, Albert</au><au>Scharl, Arno</au><au>Gindl, Stefan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams</atitle><btitle>2016 49th Hawaii International Conference on System Sciences (HICSS)</btitle><stitle>HICSS</stitle><date>2016-01-01</date><risdate>2016</risdate><spage>1040</spage><epage>1048</epage><pages>1040-1048</pages><issn>1530-1605</issn><eissn>2572-6862</eissn><eissn>1530-1605</eissn><eisbn>0769556701</eisbn><eisbn>9780769556703</eisbn><coden>IEEPAD</coden><abstract>Policy makers and environmental organizations have a keen interest in awareness building and the evolution of stakeholder opinions on environmental issues. Mere polarity detection, as provided by many existing methods, does not suffice to understand the emergence of collective awareness. Methods for extracting affective knowledge should be able to pinpoint opinion targets within a thread. Opinion target extraction provides a more accurate and fine-grained identification of opinions expressed in online media. This paper compares two different approaches for identifying potential opinion targets and applies them to comments from the YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and measures their ability to handle noisy input data from social media streams.</abstract><pub>IEEE</pub><doi>10.1109/HICSS.2016.133</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 1530-1605
ispartof	2016 49th Hawaii International Conference on System Sciences (HICSS), 2016, p.1040-1048
issn	1530-1605 2572-6862 1530-1605
language	eng
recordid	cdi_ieee_primary_7427311
source	IEEE Xplore All Conference Series
subjects	Classification climate change Conferences Digital media Earth Evolution Feature extraction keyword analysis Media Meteorology opinion mining opinion target extraction Organizations Platforms Polarity Sentences sentiment analysis Social networks Syntactics YouTube
title	Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-24T04%3A23%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Extracting%20Opinion%20Targets%20from%20Environmental%20Web%20Coverage%20and%20Social%20Media%20Streams&rft.btitle=2016%2049th%20Hawaii%20International%20Conference%20on%20System%20Sciences%20(HICSS)&rft.au=Weichselbraun,%20Albert&rft.date=2016-01-01&rft.spage=1040&rft.epage=1048&rft.pages=1040-1048&rft.issn=1530-1605&rft.eissn=2572-6862&rft.coden=IEEPAD&rft_id=info:doi/10.1109/HICSS.2016.133&rft.eisbn=0769556701&rft.eisbn_list=9780769556703&rft_dat=%3Cproquest_CHZPO%3E1816027563%3C/proquest_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i248t-532ec6b4e62d940d84336575367fc67e4c294aac23abea57fe0ff4b86f7d38743%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1816027563&rft_id=info:pmid/&rft_ieee_id=7427311&rfr_iscdi=true