Multimodal sentiment analysis: Difference between revisions

Revision as of 18:12, 23 February 2020

Revision as of 17:41, 28 February 2020

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'''Multimodal sentiment analysis''' is a new dimension{{peacock term|date=June 2018}} of the traditional text-based [[sentiment analysis]], which goes beyond the analysis of texts, and includes other [[Modality (human–computer interaction)|modalities]] such as audio and visual data.<ref>{{cite journal |last1=Soleymani |first1=Mohammad |last2=Garcia |first2=David |last3=Jou |first3=Brendan |last4=Schuller |first4=Björn |last5=Chang |first5=Shih-Fu |last6=Pantic |first6=Maja |title=A survey of multimodal sentiment analysis |journal=Image and Vision Computing |date=September 2017 |volume=65 |pages=3–14 |doi=10.1016/j.imavis.2017.08.003|url=https://zenodo.org/record/3449163 }}</ref> It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities.<ref>{{cite journal |last1=Karray |first1=Fakhreddine |last2=Milad |first2=Alemzadeh |last3=Saleh |first3=Jamil Abou |last4=Mo Nours |first4=Arab |title=Human-Computer Interaction: Overview on State of the Art |journal=International Journal on Smart Sensing and Intelligent Systems |volume=1 |pages=137–159 |date=2008 |url=http://s2is.org/Issues/v1/n1/papers/paper9.pdf|doi=10.21307/ijssis-2017-283 }}</ref> With the extensive amount of [[social media]] data available online in different forms such as videos and images, the conventional text-based [[sentiment analysis]] has evolved into more complex models of multimodal sentiment analysis,<ref name="s1">{{cite journal |last1=Poria |first1=Soujanya |last2=Cambria |first2=Erik |last3=Bajpai |first3=Rajiv |last4=Hussain |first4=Amir |title=A review of affective computing: From unimodal analysis to multimodal fusion |journal=Information Fusion |date=September 2017 |volume=37 |pages=98–125 |doi=10.1016/j.inffus.2017.02.003|hdl=1893/25490 }}</ref> which can be applied in the development of [[virtual assistant]]s,<ref name ="s5">{{cite web |title=Google AI to make phone calls for you |url=https://www.bbc.com/news/technology-44045424 |website=BBC News |accessdate=12 June 2018 |date=8 May 2018}}</ref> [[Social media analytics|analysis]] of YouTube movie reviews,<ref name="s4">{{cite journal |last1=Wollmer |first1=Martin |last2=Weninger |first2=Felix |last3=Knaup |first3=Tobias |last4=Schuller |first4=Bjorn |last5=Sun |first5=Congkai |last6=Sagae |first6=Kenji |last7=Morency |first7=Louis-Philippe |title=YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context |journal=IEEE Intelligent Systems |date=May 2013 |volume=28 |issue=3 |pages=46–53 |doi=10.1109/MIS.2013.34}}</ref> [[Social media analytics|analysis]] of news videos,<ref>{{cite arxiv|last1=Pereira |first1=Moisés H. R. |last2=Pádua |first2=Flávio L. C. |last3=Pereira |first3=Adriano C. M. |last4=Benevenuto |first4=Fabrício |last5=Dalip |first5=Daniel H. |title=Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos|date=9 April 2016 |eprint=1604.02612|class=cs.CL }}</ref> and [[emotion recognition]] (sometimes known as [[emotion]] detection) such as [[depression (mood)|depression]] monitoring,<ref name = "s6">{{cite book |last1=Zucco |first1=Chiara |last2=Calabrese |first2=Barbara |last3=Cannataro |first3=Mario |title=Sentiment analysis and affective computing for depression monitoring |journal=2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |date=November 2017 |pages=1988–1995 |doi=10.1109/bibm.2017.8217966 |publisher=IEEE |language=English|isbn=978-1-5090-3050-7 }}</ref> among others.

Similar to the traditional [[sentiment analysis]], one of the most basic task in multimodal sentiment analysis is [[Feeling|sentiment]] classification, which classifies different sentiments into categories such as positive, negative, or neutral.<ref>{{cite book |last1=Pang |first1=Bo |last2=Lee |first2=Lillian |title=Opinion mining and sentiment analysis |date=2008 |publisher=Now Publishers |___location=Hanover, MA |isbn=978-1601981509}}</ref> The complexity of [[Social media analytics|analyzing]] text, audio, and visual features to perform such a task requires the application of different fusion techniques, such as feature-level, decision-level, and hybrid fusion.<ref name="s1" /> The performance of these fusion techniques and the [[classification]] [[algorithm]]s applied, are influenced by the type of textual, audio, and visual features employed in the analysis.<ref name = "s7" />

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=== Visual features ===

One of the main advantages of analyzing videos with respect to texts alone, is the presence of rich sentiment cues in visual data.<ref>{{cite journal |last1=Poria |first1=Soujanya |last2=Cambria |first2=Erik |last3=Hazarika |first3=Devamanyu |last4=Majumder |first4=Navonil |last5=Zadeh |first5=Amir |last6=Morency |first6=Louis-Philippe |title=Context-Dependent Sentiment Analysis in User-Generated Videos |journal=Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |pages=873–883 |date=2017 |doi=10.18653/v1/p17-1081 }}</ref> Visual features include [[facial expression]]s, which are of paramount importance in capturing sentiments and [[emotion]]s, as they are a main channel of forming a person's present state of mind.<ref name="s1" /> Specifically, [[smile]], is considered to be one of the most predictive visual cues in multimodal sentiment analysis.<ref name="s2" /> OpenFace is an open-source facial analysis toolkit available for extracting and understanding such visual features.<ref>{{cite journal |title=OpenFace: An open source facial behavior analysis toolkit - IEEE Conference Publication |journal= |urldoi=https: 10.1109//ieeexploreWACV.ieee2016.org/document/7477553/}}</ref>

== Fusion techniques ==

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=== Hybrid fusion ===

Hybrid fusion is a combination of feature-level and decision-level fusion techniques, which exploits complementary information from both methods during the classification process.<ref name="s4" /> It usually involves a two-step procedure wherein feature-level fusion is initially performed between two modalities, and decision-level fusion is then applied as a second step, to fuse the initial results from the feature-level fusion, with the remaining [[Modality (human–computer interaction)|modality]].<ref>{{cite journal |last1=Shahla |first1=Shahla |last2=Naghsh-Nilchi |first2=Ahmad Reza |title=Exploiting evidential theory in the fusion of textual, audio, and visual modalities for affective music video retrieval - IEEE Conference Publication |journal= |date=2017 |urldoi=https:10.1109//ieeexplorePRIA.ieee2017.org/abstract/document/7983051/ }}</ref><ref>{{cite journal |last1=Poria |first1=Soujanya |last2=Peng |first2=Haiyun |last3=Hussain |first3=Amir |last4=Howard |first4=Newton |last5=Cambria |first5=Erik |title=Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis |journal=Neurocomputing |date=October 2017 |volume=261 |pages=217–230 |doi=10.1016/j.neucom.2016.09.117}}</ref>

== Applications ==