  With the increasing popularity of video sharing websites such as YouTube and
Facebook, multimodal sentiment analysis has received increasing attention from
the scientific community. Contrary to previous works in multimodal sentiment
analysis which focus on holistic information in speech segments such as bag of
words representations and average facial expression intensity, we develop a
novel deep architecture for multimodal sentiment analysis that performs
modality fusion at the word level. In this paper, we propose the Gated
Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is
composed of 2 modules. The Gated Multimodal Embedding alleviates the
difficulties of fusion when there are noisy modalities. The LSTM with Temporal
Attention performs word level fusion at a finer fusion resolution between input
modalities and attends to the most important time steps. As a result, the
GME-LSTM(A) is able to better model the multimodal structure of speech through
time and perform better sentiment comprehension. We demonstrate the
effectiveness of this approach on the publicly-available Multimodal Corpus of
Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving
state-of-the-art sentiment classification and regression results. Qualitative
analysis on our model emphasizes the importance of the Temporal Attention Layer
in sentiment prediction because the additional acoustic and visual modalities
are noisy. We also demonstrate the effectiveness of the Gated Multimodal
Embedding in selectively filtering these noisy modalities out. Our results and
analysis open new areas in the study of sentiment analysis in human
communication and provide new models for multimodal fusion.
