Sentiment analysis is the task of classifying the polarity of a given text.
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While the general task of textual sentiment classification has been widely studied, much less research looks specifically at sentiment between a specified source and target.
Multimodal language analysis is an emerging research area in natural language processing that models language in a multimodal manner.
In order to extract aspect and opinion terms for Indonesian hotel reviews, we adapt double embeddings feature and attention mechanism that outperform the best system at SemEval 2015 and 2016.
Recently, the pre-trained language model, BERT, has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.
This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks.
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.
The Philippines is a common ground to natural calamities like typhoons, floods, volcanic eruptions and earthquakes.