Unsupervised Domain Adaptation for Sentimental Classification by Word Embeddings on the Lower Layer of BERT Bidirectional Encoder Representations from Transformers (BERT) is a stacked (12 or 24) model of multihead attention applied in the transformers. The multihead attention of each layer outputs a word embedded expression sequence corresponding to the input word sequence. When BERT is applied to the feature base, the output is the wordembedded expression column of the highest layer used in each task. On the other hand, in domain adaptation, projecting the data of each region onto the common subspace of the source and target domains is an effective approach. When constructing a feature vector on a common subspace from a wordembedded representation output of the BERT, the most significant layer depends on the task learning task assigned to BERT, so is not necessarily more significant than the wordembedded representation of a lower layer. Layers are suboptimal for regional adaptation. Here we confirm this concept on unsupervised domain adaptation of an emotion analysis.

AspectBased Sentiment Analysis with Adjustments to Irrelevant SentimentalRelated Features Aspectbased sentiment analysis is a finegrained task of sentiment classification for multiple aspects in a sentence. While the overall polarity of a sentence has been successfully predicted with neural network architectures, aspectspecific sentiment analysis still faces challenges. In this paper, we propose a novel neural network named IRPBERT. It is based on BERT which is the newest breakthrough in natural language processing. IRPBERT aims at improving the accuracy of predicting the right polarity of aspects in a sentence, especially the neutral ones, because some sentimentalrelated words such as love in a noun phrase which is neutrally sentimental could have a wrong impact on the polarity classification to aspects. More specifically, we design a module named IrrelevanceRemoving Process (IRP) to counteract the impact from irrelevant sentimentalrelated features. The final prediction depends on word representations from the outputs of both IRP and BERT. Experiments on a twitter dataset show that our model outperforms both existing stateoftheart methods and finetuned BERT baselines.