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Multimodal Sentiment Analysis

Multimodal sentiment analysis is a technology that combines traditional text-based sentiment analysis with modalities such as audio and visual data. It can be bimodal, meaning it includes combinations of two modalities, or trimodal, which incorporates three modalities.

Similar to Traditional Sentiment Analysis

One of the most basic tasks 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 this task requires the application of different fusion techniques.

Feature-Level Fusion

Feature-level fusion involves gathering all the features from each modality (text, audio, or visual) and joining them into a single feature vector, which is then fed into a classification algorithm. This technique is known as early fusion.

Decision-Level Fusion

Decision-level fusion involves feeding data from each modality independently into its own classification algorithm and combining the results to obtain the final sentiment classification. This technique is known as late fusion.

Hybrid Fusion

Hybrid fusion combines features-level and decision-level fusion techniques, allowing for the exploitation of complementary information from both methods. It usually involves a two-step procedure, where feature-level fusion is first applied between two modalities, followed by decision-level fusion as a second step.

Applications

Multimodal sentiment analysis can be applied in various fields, including the development of virtual assistants, analysis of user-generated videos of movie reviews, and general product reviews. It also plays an important role in the advancement of virtual assistants through the application of natural language processing (NLP) and machine learning techniques. In the healthcare domain, multimodal sentiment analysis can be utilized to detect certain medical conditions such as stress, anxiety, or depression. 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.