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{{Short description|Technology for sentiment analysis}}
'''Multimodal sentiment analysis''' is a
Similar to the traditional [[
== Features ==
[[Feature engineering]], which involves the selection of features that are fed into [[machine learning]] algorithms, plays a key role in the sentiment classification performance.<ref name = "s7">{{cite journal |last1=Sun |first1=Shiliang |last2=Luo |first2=Chen |last3=Chen |first3=Junyu |title=A review of natural language processing techniques for opinion mining systems |journal=Information Fusion |date=July 2017 |volume=36 |pages=10–25 |doi=10.1016/j.inffus.2016.10.004}}</ref> In multimodal sentiment analysis, a combination of different textual, audio, and visual features are employed.<ref name = "s1"
=== Textual
Similar to the conventional text-based [[
=== Audio
[[Feeling|Sentiment]] and [[emotion]] characteristics are prominent in different [[phonetic]] and [[prosodic]] properties contained in audio features.<ref>{{cite journal |last1=Chung-Hsien Wu |last2=Wei-Bin Liang |title=Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels |journal=IEEE Transactions on Affective Computing |date=January 2011 |volume=2 |issue=1 |pages=10–21 |doi=10.1109/T-AFFC.2010.16|s2cid=52853112 }}</ref> Some of the most important audio features employed in multimodal sentiment analysis are [[mel-frequency cepstrum| mel-frequency cepstrum (MFCC)]], [[spectral centroid]], [[spectral flux]],
=== Visual features ===
▲[[Feeling|Sentiment]] and [[emotion]] characteristics are prominent in different [[phonetic]] and [[prosodic]] properties contained in audio features.<ref>{{cite journal |last1=Chung-Hsien Wu |last2=Wei-Bin Liang |title=Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels |journal=IEEE Transactions on Affective Computing |date=January 2011 |volume=2 |issue=1 |pages=10–21 |doi=10.1109/T-AFFC.2010.16}}</ref> Some of the most important audio features employed in multimodal sentiment analysis are [[mel-frequency cepstrum| mel-frequency cepstrum (MFCC)]], [[spectral centroid]], [[spectral flux]], [[beat]]{{dn|date=June 2018}} histogram, beat sum, strongest beat, pause duration, and [[pitch accent|pitch]].<ref name="s1"></ref> [[OpenSMILE]]<ref>{{cite journal |last1=Eyben |first1=Florian |last2=Wöllmer |first2=Martin |last3=Schuller |first3=Björn |title=OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit - IEEE Conference Publication |journal=ieeexplore.ieee.org |date=2009 |doi=10.1109/ACII.2009.5349350 |url=http://ieeexplore.ieee.org/document/5349350}}</ref> and [[Praat]] are popular open-source toolkits for extracting such audio features.<ref>{{cite journal |last1=Morency |first1=Louis-Philippe |last2=Mihalcea |first2=Rada |last3=Doshi |first3=Payal |title=Towards multimodal sentiment analysis: harvesting opinions from the web |date=14 November 2011 |pages=169–176 |doi=10.1145/2070481.2070509 |url=https://dl.acm.org/citation.cfm?id=2070509 |publisher=ACM}}</ref>
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 |
Unlike the traditional text-based [[
▲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) |date=2017 |doi=10.18653/v1/p17-1081 |url=https://doi.org/10.18653/v1/P17-1081 |publisher=Association for Computational Linguistics}}</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"></ref> Specifically, [[smile]], is considered to be one of the most predictive visual cues in multimodal sentiment analysis.<ref name="s2"></ref> 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=ieeexplore.ieee.org |url=https://ieeexplore.ieee.org/document/7477553/}}</ref>
Feature-level fusion (sometimes known as early fusion) gathers all the features from each [[modality (human–computer interaction)|modality]] (text, audio, or visual) and joins them together into a single feature vector, which is eventually fed into a classification algorithm.<ref name="s3">{{cite journal |last1=Poria |first1=Soujanya |last2=Cambria |first2=Erik |last3=Howard |first3=Newton |last4=Huang |first4=Guang-Bin |last5=Hussain |first5=Amir |title=Fusing audio, visual and textual clues for sentiment analysis from multimodal content |journal=Neurocomputing |date=January 2016 |volume=174 |pages=50–59 |doi=10.1016/j.neucom.2015.01.095|s2cid=15287807 }}</ref>
===
Decision-level fusion (sometimes known as late fusion), feeds data from each modality (text, audio, or visual) independently into its own classification algorithm, and obtains the final sentiment classification results by fusing each result into a single decision vector.<ref name="s3"
▲Unlike the traditional text-based [[Sentiment analysis|sentiment analysis]], multimodal sentiment analysis undergo a fusion process in which data from different modalities (text, audio, or visual) are fused and analyzed together.<ref name ="s1"></ref> The existing approaches in multimodal sentiment analysis [[data fusion]] can be grouped into three main categories: feature-level, decision-level, and hybrid fusion, and the performance of the sentiment classification depends on which type of fusion technique is employed.<ref name ="s1"></ref>
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"
▲=== Feature-level Fusion ===
▲Feature-level fusion (sometimes known as early fusion) gathers all the features from each [[modality (human–computer interaction)|modality]] (text, audio, or visual) and joins them together into a single feature vector, which is eventually fed into a classification algorithm<ref name="s3">{{cite journal |last1=Poria |first1=Soujanya |last2=Cambria |first2=Erik |last3=Howard |first3=Newton |last4=Huang |first4=Guang-Bin |last5=Hussain |first5=Amir |title=Fusing audio, visual and textual clues for sentiment analysis from multimodal content |journal=Neurocomputing |date=January 2016 |volume=174 |pages=50–59 |doi=10.1016/j.neucom.2015.01.095}}</ref>. One of the difficulties in implementing this technique is the integration of the heterogeneous features.<ref name="s1"></ref>
▲Decision-level fusion (sometimes known as late fusion), feeds data from each modality (text, audio, or visual) independently into its own classification algorithm, and obtains the final sentiment classification results by fusing each result into a single decision vector.<ref name="s3"></ref> One of the advantages of this fusion technique is that it eliminates the need to fuse heterogeneous data, and each [[modality (human–computer interaction)|modality]] can utilize its most appropriate [[Classification|classification]] [[Algorithm|algorithm]].<ref name="s1"></ref>
▲=== 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"></ref> 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=ieeexplore.ieee.org |date=2017 |url=https://ieeexplore.ieee.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 ==
Similar to text-based sentiment analysis, multimodal sentiment analysis can be applied in the development of different forms of [[recommender system]]s such as in the analysis of user-generated videos of movie reviews<ref name="s4"
==References==▼
▲Similar to text-based sentiment analysis, multimodal sentiment analysis can be applied in the development of different forms of [[recommender system]]s such as in the analysis of user-generated videos of movie reviews<ref name="s4"></ref> and general product reviews<ref>{{cite journal |last1=Pérez-Rosas |first1=Verónica |last2=Mihalcea |first2=Rada |last3=Morency |first3=Louis Philippe |title=Utterance-level multimodal sentiment analysis |journal=Long Papers |date=1 January 2013 |url=https://experts.umich.edu/en/publications/utterance-level-multimodal-sentiment-analysis |publisher=Association for Computational Linguistics (ACL)}}</ref>, to predict the sentiments of customers, and subsequently create product or service recommendations.<ref>{{cite web |last1=Chui |first1=Michael |last2=Manyika |first2=James |last3=Miremadi |first3=Mehdi |last4=Henke |first4=Nicolaus |last5=Chung |first5=Rita |last6=Nel |first6=Pieter |last7=Malhotra |first7=Sankalp |title=Notes from the AI frontier. Insights from hundreds of use cases |url=https://www.mckinsey.com/mgi/ |website=McKinsey & Company |publisher=McKinsey & Company |accessdate=13 June 2018 |language=en}}</ref> Multimodal sentiment analysis also plays an important role in the advancement of [[virtual assistant]]s through the application of [[natural language processing (NLP) and [[machine learning]] techniques.<ref name ="s5"></ref> In the healthcare ___domain, multimodal sentiment analysis]] can be utilized to detect certain medical conditions such as [[Psychological stress|stress]], [[anxiety]], or [[Depression (mood)|depression]].<ref name = "s6"></ref> Multimodal sentiment analysis can also be applied in understanding the sentiments contained in video news programs, which is considered as a complicated and challenging ___domain, as sentiments expressed by reporters tend to be less obvious or neutral.<ref>{{cite journal |last1=Ellis |first1=Joseph G. |last2=Jou |first2=Brendan |last3=Chang |first3=Shih-Fu |title=Why We Watch the News: A Dataset for Exploring Sentiment in Broadcast Video News |date=12 November 2014 |pages=104–111 |doi=10.1145/2663204.2663237 |url=https://dl.acm.org/citation.cfm?doid=2663204.2663237 |publisher=ACM}}</ref>
{{Reflist}}
▲==References==
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Machine learning]]
[[Category:Multimodal interaction]]
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