Leveraging multiple features for document sentiment classification Abstract   Sentiment classification is an important research task in Natural Language Processing. To fulfill this type of classification, previous works have focused on leveraging taskspecific features. However, they only notice part of the related features. Also, stateoftheart methods based on neural networks often ignore traditional features. This paper proposes a novel text sentiment classification method that learns the representation of texts by hierarchically incorporating multiple features. More specifically, we design different representations for sentiment words according to the polarity of labeled texts and whether negation exists; we distinguish words with different partofspeech tags; emoticons, if there are, are to optimize the word vectors obtained in the previous step; apart from word embeddings, character embeddings are also trained. We use a deep neural network to get a sentencelevel representation from both word and character sequence. For documents with at least two sentences, we use a hierarchical structure and design a rule to give more weight to import sentences empirically to get a documentlevel representation. Experimental results on open datasets demonstrate that our method could effectively improve the sentiment classification performance compared with the basic models and stateoftheart methods.

Learning Local and Global Multicontext Representations for Document Classification Contextbased attention models achieve stateoftheart results on constructing document representations. Current methods build document representations based on a trainable global context vector for all documents, while local contexts capturing documentspecific information are ignored. Representing document context as a vector also restricts the modelu0027s capacity to capture complete semantic components of documents. In this work, we address these limitations by proposing novel attentionbased approaches. We first propose a local and global context attention (LGCA) model, which constructs the context representations of a document as a combination of global and local contexts. The local context is used to capture local documentspecific information. Furthermore, a multicontext attention (MCA) model is proposed to capture multiple local contexts within a document, thereby fusing the overall semantic information of documents. We conduct experiments on documentlevel sentiment classification tasks where extra user and product information is shown to be important. Experimental results indicate that our methods significantly outperform previous models with and without user and product information. It is also demonstrated that our model has great potential of handling extra information to improve document classification performance.