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Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. [1] It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. [ 2 ]
Figure 1. A simple bimodal distribution, in this case a mixture of two normal distributions with the same variance but different means. The figure shows the probability density function (p.d.f.), which is an equally-weighted average of the bell-shaped p.d.f.s of the two normal distributions.
A bimodal distribution would have two high points rather than one. The shape of a distribution is sometimes characterised by the behaviours of the tails (as in a long or short tail). For example, a flat distribution can be said either to have no tails, or to have short tails.
A simple bimodal distribution. Figure 3. A bimodal distribution. Note that only the largest peak would correspond to a mode in the strict sense of the definition of mode. In statistics, a unimodal probability distribution or unimodal distribution is a probability distribution which has a single peak.
Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. [31] It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. [ 32 ]
The basic form as given by Box and Muller takes two samples from the uniform distribution on the interval (0,1) and maps them to two standard, normally distributed samples. The polar form takes two samples from a different interval, [−1,+1] , and maps them to two normally distributed samples without the use of sine or cosine functions.
Special cases are conditionally uniform random graphs, where assigns equal probability to all the graphs having specified properties. They can be seen as a generalization of the Erdős–Rényi model G ( n , M ), when the conditioning information is not necessarily the number of edges M , but whatever other arbitrary graph property P ( G ...
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