  Multi-modal sentiment analysis plays an important role for providing better
interactive experiences to users. Each modality in multi-modal data can provide
different viewpoints or reveal unique aspects of a user's emotional state. In
this work, we use text, audio and visual modalities from MOSI dataset and we
propose a novel fusion technique using a multi-head attention LSTM network.
Finally, we perform a classification task and evaluate its performance.
