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.

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.