Opinion question answering by sentiment clip localization

This article considers multimedia question answering beyond factoid and how-to questions. We are interested in searching videos for answering opinion-oriented questions that are controversial and hotly debated. Examples of questions include "Should Edward Snowden be pardoned?" and "Ob...

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Main Authors: PANG, Lei, NGO, Chong-wah
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/6359
https://ink.library.smu.edu.sg/context/sis_research/article/7362/viewcontent/2818711.pdf
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spelling sg-smu-ink.sis_research-73622021-11-23T03:45:14Z Opinion question answering by sentiment clip localization PANG, Lei NGO, Chong-wah This article considers multimedia question answering beyond factoid and how-to questions. We are interested in searching videos for answering opinion-oriented questions that are controversial and hotly debated. Examples of questions include "Should Edward Snowden be pardoned?" and "Obamacare-unconstitutional or not?". These questions often invoke emotional response, either positively or negatively, hence are likely to be better answered by videos than texts, due to the vivid display of emotional signals visible through facial expression and speaking tone. Nevertheless, a potential answer of duration 60s may be embedded in a video of 10min, resulting in degraded user experience compared to reading the answer in text only. Furthermore, a text-based opinion question may be short and vague, while the video answers could be verbal, less structured grammatically, and noisy because of errors in speech transcription. Direct matching of words or syntactic analysis of sentence structure, such as adopted by factoid and how-to question-answering, is unlikely to find video answers. The first problem, the answer localization, is addressed by audiovisual analysis of the emotional signals in videos for locating video segments likely expressing opinions. The second problem, questions and answers matching, is tackled by a deep architecture that nonlinearly matches text words in questions and speeches in videos. Experiments are conducted on eight controversial topics based on questions crawled from Yahoo! Answers and Internet videos from YouTube. 2016-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6359 info:doi/10.1145/2818711 https://ink.library.smu.edu.sg/context/sis_research/article/7362/viewcontent/2818711.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Algorithms Performance Experimentation Multimedia question answering opinion clip localization multimodality sentiment analysis Graphics and Human Computer Interfaces Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Algorithms
Performance
Experimentation
Multimedia question answering
opinion clip localization
multimodality sentiment analysis
Graphics and Human Computer Interfaces
Theory and Algorithms
spellingShingle Algorithms
Performance
Experimentation
Multimedia question answering
opinion clip localization
multimodality sentiment analysis
Graphics and Human Computer Interfaces
Theory and Algorithms
PANG, Lei
NGO, Chong-wah
Opinion question answering by sentiment clip localization
description This article considers multimedia question answering beyond factoid and how-to questions. We are interested in searching videos for answering opinion-oriented questions that are controversial and hotly debated. Examples of questions include "Should Edward Snowden be pardoned?" and "Obamacare-unconstitutional or not?". These questions often invoke emotional response, either positively or negatively, hence are likely to be better answered by videos than texts, due to the vivid display of emotional signals visible through facial expression and speaking tone. Nevertheless, a potential answer of duration 60s may be embedded in a video of 10min, resulting in degraded user experience compared to reading the answer in text only. Furthermore, a text-based opinion question may be short and vague, while the video answers could be verbal, less structured grammatically, and noisy because of errors in speech transcription. Direct matching of words or syntactic analysis of sentence structure, such as adopted by factoid and how-to question-answering, is unlikely to find video answers. The first problem, the answer localization, is addressed by audiovisual analysis of the emotional signals in videos for locating video segments likely expressing opinions. The second problem, questions and answers matching, is tackled by a deep architecture that nonlinearly matches text words in questions and speeches in videos. Experiments are conducted on eight controversial topics based on questions crawled from Yahoo! Answers and Internet videos from YouTube.
format text
author PANG, Lei
NGO, Chong-wah
author_facet PANG, Lei
NGO, Chong-wah
author_sort PANG, Lei
title Opinion question answering by sentiment clip localization
title_short Opinion question answering by sentiment clip localization
title_full Opinion question answering by sentiment clip localization
title_fullStr Opinion question answering by sentiment clip localization
title_full_unstemmed Opinion question answering by sentiment clip localization
title_sort opinion question answering by sentiment clip localization
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/6359
https://ink.library.smu.edu.sg/context/sis_research/article/7362/viewcontent/2818711.pdf
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