Multimodal sentiment analysis

Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. 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, which can be applied in the development of virtual assistants, analysis of YouTube movie reviews, analysis of news videos, and emotion recognition (sometimes known as emotion detection) such as depression monitoring, among others. Similar to the traditional sentiment analysis, one of the most basic task in multimodal sentiment analysis is sentiment classification, which classifies different sentiments into categories such as positive, negative, or neutral. The complexity of 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. The performance of these fusion techniques and the classification algorithms applied, are influenced by the type of textual, audio, and visual features employed in the analysis. Feature engineering, which involves the selection of features that are fed into machine learning algorithms, plays a key role in the sentiment classification performance. In multimodal sentiment analysis, a combination of different textual, audio, and visual features are employed. Similar to the conventional text-based sentiment analysis, some of the most commonly used textual features in multimodal sentiment analysis are unigrams and n-grams, which are basically a sequence of words in a given textual document. 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.