Abstract
The main aim of this book was to go beyond textual sentiment analysis approaches by integrating audio and visual features with textual for multimodal sentiment analysis. To this end, textual sentiment analysis has also been improved by further developing and applying common-sense computing and linguistic patterns to bridge the cognitive and affective gap between word-level natural language data and the concept-level opinions conveyed by these. Various novel linguistic and machine learning based frameworks have been developed in order to accomplish multimodal sentiment analysis. Apart from the sentiment analysis task, the proposed multimodal model is also capable to detect emotions in videos.
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Poria, S., Hussain, A., Cambria, E. (2018). Conclusion and Future Work. In: Multimodal Sentiment Analysis. Socio-Affective Computing, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-95020-4_8
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DOI: https://doi.org/10.1007/978-3-319-95020-4_8
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