# LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification
## 1 Introduction  
Aspect-based sentiment classification (ABSC) [1; 2] is a fine-grained Natural Language Processing (NLP) task, and it is a significant branch of sentiment analysis [3; 4; 5; 6]. Traditional sentiment analysis approaches [7; 8; 9; 10] mainly focus on inferring sentence-level or document-level sentiment polarities (typically positive, negative, or neutral in triple-classification). However, the ABSC task, also known as aspect-based sentiment analysis (ABSA), differs from traditional sentiment analysis. It aims to predict independent sentiment polarities for targeted aspects within the same sentence or document. Aspect-based sentiment classification local contexts people make full use of the sentiment polarity, subjectivity and further information from targeted aspects. The ABSC datasets are composed of plenty of contexts and aspects. For example, reviews from customers usually comment on different aspects, and different aspects may deliver different sentiment polarities. Given sentences like \"while the food is so good and so popular that waiting can really be a nightmare\". Definitely, the customer compliments the food but criticizes the service. Due to certain reasons, the restaurant makes the customer wait for a long while. Generally, traditional sentence-level or document-level sentiment polarity mining methods cannot precisely predict polarities for specific aspects as they do not consider the fine-grained polarities of different aspects. Deep Neural Networks (DNNs), such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), are employed in NLP tasks in recent years, and those DNN-based models areproven to be efficient as well as an effective way to learn and deal with features of documents, sentences, words, and even tokens. Self-attention, which has been introduced in transformer [11] architecture, is a new attention mechanism compared to the traditional attention mechanism [12; 13; 14] to capture internal correlation of input representations. Experimental results have proved that self-attention performs much better than traditional DNN architectures in capturing features for Sequence to Sequence (Seq2 Seq) tasks. BERT [15] is a pretrained linguistic model that can be adapted to many NLP tasks; it attained state-of-the-art performance on 11 NLP tasks. LCF design incorporates pretrained BERT model with LCF design as shared layers, and it outperforms state-of-the-art performance among three ABSC datasets. Almost none of the previous studies took the significant emotional information contained in aspect's local context into considerations. Indeed, traditional DNN-based methods only focus on analyzing the correlations of the global context and sentiment polarities before identifying the sentiment polarity of targeted aspect based on global context features. However, LCF design notices that sentiment polarity of an aspect is more relevant to the context words near to itself. Moreover, the context words far from the aspect probably tend to cause negative influence to precisely predict the polarity of specific aspect (see Figures 1 and 2). For example, semantics, adjectives, adverbs, and other subjective presentations are usually placed to the aspects which they modify. Further, they are more semantic-relative to the aspect which they are closed to. In that case, Semantic-Relative Distance (SRD) (see Section 3.2) is proposed to determine if a contextual word is the local context of a specific aspect. CDM and CDW layers concentrate on local context with the help of SRD. Based on Multi-Head Self-Attention (MHSA) different from previous methods, LCF design models learn the features from the sequence-level global context, but also from local context words associated with a specific aspect. LCF design compute local context features according to SRD. The main contributions of this paper are as follows:  
* This paper proposes LCF design models, which utilize self-attention to capture local context features and global context features concurrently.