Multimodal Bag-of-Words for Cross Domains Sentiment Analysis

Abstract

The advantages of using cross domain data when performing text-based sentiment analysis have been established; however, similar findings have yet to be observed when performing multimodal sentiment analysis. A potential reason for this is that systems based on feature extracted from speech and facial features are susceptible to confounding effecting caused by different recording conditions associated with data collected in different locations. In this regard, we herein explore different Bag-of-Words paradigms to aid sentiment detection by providing training material from an additional dataset. Key results presented indicate that using a Bag-of-Words extraction paradigm that takes into account information from both the test domain and the out of domain datasets yields gains in system performance.