# A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification
## 1 Introduction  
Aspect-level sentiment classification, or aspect-based sentiment analysis (ABSA), is a fine-grained sentence analysis, which recognizes the aspects mentioned in the sentence and their corresponding sentiment polarity [1]. ABSA task consists of two subtasks, aspect category detection and sentiment polarity classification. For instance, given the sentence \"The food is not cheap but quite delicious\", first the two aspects of sentiment target food are extracted: FOOD#PRICE and FOOD#TASTE. Then, the sentiment for each aspect is classified: negative for \"not cheap\" in FOOD#PRICE and positive for \"quite delicious\" in FOOD#TASTE. ABSA task has become one of the research focuses in the sentiment analysis domain of natural language processing (NLP) [2, 3]. Conventional machine learning methods such as Support Vector Machine (SVM) have been applied to the text classification issues [4], and can also be used in sentiment analysis. These methods extract features in the sentence to build the model, and use these features to predict the categories of non-annotated data. There have been many achievements in the machine learning approaches [4, 5]. However, conventional machine learning approaches have an obvious drawback that they treat the sentence as a bag of words and do not consider the sequence and the relationships between aspect terms and its corresponding opinion-indicating words, when the sentiment polarity is often determined by such sequence or relationships. Another problem is that thesemodels depend too much on the feature engineering and require a quantity of manual preprocessing, which stops them from being more efficient or accurate. [PERSON] et al. [6] pointed out that considering target information tends to reduce errors about 40% in the sentiment classification tasks. Deep learning methods, especially recurrent neural network (RNN) [7], consider the context when modeling the sentence, which enable them to analyze target information and its context word dependencies. [PERSON] et al. [8] introduced a self-adaptive recursive neural network AdaRNN. The model analyzes the sentiment structure and context information to learn the sentiment relationships between target and other words in the sentence. [PERSON] et al. [9] combined recursive neural network with dependency tree and presented PhraseRNN. The model builds a binary phrase dependency tree for features in different aspects and judges sentiment polarity of different aspects with the dependency tree. [PERSON] et al. [10] proposed Target-Dependent Long Short-Term Memory (TD-LSTM). The model uses two LSTM network, which, respectively, propagate the pre-context and back propagate the post-context, to evaluate the target in the context. In addition to target information analysis, attention mechanism is put forward to promote the accuracy of the aspect-level sentiment analysis in recent years [11]. The attention mechanism is an imitation of human's behavior that, when a man is reading some text, he tends to focus on some specific words or phrases that are important for understanding the text. Attention is a vector whose length is equal to the sentence, and the value of each dimension represents the importance rank of the corresponding word. The attention vector usually element-wise multiplies the sentence representation, which allows the model to focus on the important parts of the sentence. [PERSON] et al. [11] proposed an attention-based LSTM network, who applies attention on the LSTM hidden layer through multiplication with aspect embedding vector. The model focuses on the aspect information and has made great progress in the ABSA tasks. [PERSON] et al. [12] raised an attention-based memory network which has two attentions, content-level attention and aspect position attention. The two attentions enhance the model's ability to extract the features of targets from sentence with multi-aspect, which makes up the shortage of single attention models. [PERSON] et al. [13] introduced a CNN-based attention model.