EmojisBased Recurrent Neural Network For Chinese Microblogs Sentiment Analysis As a novel graphical expression derived from emoticons, emojis have a wide application in social networks. Emojis can assist people in expressing stronger sentiment or show subtler sentiment indirectly which are helpful to sentiment analysis. In this paper, we propose an emojisbased recurrent neural network for sentiment analysis in Chinese microblogs. Firstly, we differentiate ambiguous emojis and explicit emojis by using pretrained word embedding and a new sentiment lexicon. Then we verify that usersu0027 information can eliminate the ambiguity of ambiguous emojis to some extent and confirm the sentiment polarity of ambiguous emojis. On the basis, we obtain emoji representations by utilizing the position vector, semantic vector and sentiment vector of emojis, then put the emoji representations into Bidirectional gated recurrent unit(BiGRU) neural network model to conduct sentiment analysis. The experimental results on a Chinese microblog dataset demonstrate that compared with other baselines, the proposed model can improve the accuracy significantly in sentiment analysis.

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.