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=== Textual features ===
Similar to the conventional text-based [[sentiment analysis]], some of the most commonly used textual features in multimodal sentiment analysis are [[n-grams|unigrams]] and [[n-gram]]s, which are basically a sequence of words in a given textual document.<ref>{{cite journal |last1=Yadollahi |first1=Ali |last2=Shahraki |first2=Ameneh Gholipour |last3=Zaiane |first3=Osmar R. |title=Current State of Text Sentiment Analysis from Opinion to Emotion Mining |journal=ACM Computing Surveys |date=25 May 2017 |volume=50 |issue=2 |pages=1–33 |doi=10.1145/3057270|s2cid=5275807 }}</ref> These features are applied using [[bag-of-words]] or bag-of-concepts feature representations, in which words or concepts are represented as vectors in a suitable space.<ref name="s2">{{cite journal |last1=Perez Rosas |first1=Veronica |last2=Mihalcea |first2=Rada |last3=Morency |first3=Louis-Philippe |title=Multimodal Sentiment Analysis of Spanish Online Videos |journal=IEEE Intelligent Systems |date=May 2013 |volume=28 |issue=3 |pages=38–45 |doi=10.1109/MIS.2013.9|s2cid=1132247 }}</ref><ref>{{cite journal |last1=Poria |first1=Soujanya |last2=Cambria |first2=Erik |last3=Hussain |first3=Amir |last4=Huang |first4=Guang-Bin |title=Towards an intelligent framework for multimodal affective data analysis |journal=Neural Networks |date=March 2015 |volume=63 |pages=104–116 |doi=10.1016/j.neunet.2014.10.005|pmid=25523041 |hdl=1893/21310 |s2cid=342649 |hdl-access=free }}</ref>
=== Audio features ===
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=== 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|s2cid=15287807 }}</ref> One of the difficulties in implementing this technique is the integration of the heterogeneous features.<ref name="s1" />
=== Decision-level fusion ===
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