Sentiment analysis is the task of classifying the polarity of a given text.
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While many sentiment classification solutions report high accuracy scores in product or movie review datasets, the performance of the methods in niche domains such as finance still largely falls behind.
In particular, we propose a tree-based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation.
The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks.
Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC).
SOTA for Aspect-Based Sentiment Analysis on SemEval 2014 Task 4 Sub Task 2 (using extra training data)
Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few.
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classification task that takes an aspect and a sentence containing the aspect and outputs the polarity of the aspect in that sentence.
Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate.