Sentiment analysis is the task of classifying the polarity of a given text.
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Opinion mining in the web becomes more and more an attracting task, due the increasing need for individuals and societies to track the mood of people against several subjects of daily life (sports, politics, television,...).
Sentiment analysis afford detecting polarity (positive, negative, neutral) in a expressed opinion and therefore classifying those reviews.
In todays competitive business world, being aware of customer needs and market-oriented production is a key success factor for industries.
Aiming at the issue, we propose a sentiment analysis and key entity detection approach based on BERT, which is applied in online financial text mining and public opinion analysis in social media.
Next, we develop a DistanceNet model which uses these distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.
Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data.
In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE.
In this paper, we combine contextual and supervised information with the general semantic representations of words occurring in the dictionary.
Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification.
Many real-world phenomena are observed at multiple resolutions.