Sentiment analysis is the task of classifying the polarity of a given text.
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Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.
We performed experiments utilizing different methods of model ensemble.
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
#2 best model for Semantic Textual Similarity on STS Benchmark
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Due to the increased availability of online reviews, sentiment analysis had been witnessed a booming interest from the researchers.
The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.
Supervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance.
In document-level sentiment classification, each document must be mapped to a fixed length vector.
SOTA for Sentiment Analysis on IMDb
Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously.