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Sentiment analysis is a significant task in Natural Language Processing. It refers to classification based on the emotional tendency in text by extracting text features. The existing results show that models based on RNN and CNN have good performance. In order to improve the performance of text sentiment analysis, we reformulate the classification task as a comparing problem, and propose Comparison Enhanced Bi-LSTM with Multi-Head Attention (CE-B-MHA). In fact, it is efficient to classify by comparison mechanism instead of doing complex calculation. In this model, bidirectional LSTM is used for initial feature extraction, and valuable information is extracted from different dimensions and representation subspaces by Multi-Head Attention. The comparison mechanism aims to score the feature vectors by comparing with the labeled vectors. The experimental results show that CE-B-MHA has better performance than many existing models on three sentiment analysis datasets. INDEX TERMS Sentiment analysis, machine learning, neural networks.

Emotion cause extraction is a challenging task for the fine-grained emotion analysis. Even though a few studies have addressed the task using clause-level classification methods, most of them have partly ignored emotion-level context information. To comprehensively leverage the information, we propose a novel method based on learning to rank to identify emotion causes from an information retrieval perspective. Our method seeks to rank candidate clauses with respect to certain provoked emotions in analogy with query-level document ranking in information retrieval. To learn effective clause ranking models, we represent candidate clauses as feature vectors involving both emotion-independent features and emotion-dependent features. Emotion-independent features are extracted to capture the possibility that a clause is expected to provoke an emotion, and emotion-dependent features are extracted to capture the relevance between candidate cause clauses and their corresponding emotions. We investigate three approaches to learning to rank for emotion cause extraction in our method. We evaluate the performance of our method on an existing dataset for emotion cause extraction. The experimental results show that our method is effective in emotion cause extraction, significantly outperforming the state-of-the-art baseline methods in terms of the precision, recall, and F-measure. INDEX TERMS Emotion analysis, emotion cause extraction, natural language processing, sentiment analysis, learning to rank.

BackgroundThe baseline incidence of the adverse events of statin therapy varies between countries. Notably, Chinese patients seem more susceptible to myopathy induced by simvastatin.ObjectivesThis research studies the adverse drug reactions (ADRs) of statin therapy in China by analysing trial-based data from the Anti-hyperlipidaemic Drug Database built by the China National Medical Products Administration Information Centre.MethodsAll clinical trials involving statin therapy (including simvastatin, atorvastatin, fluvastatin, lovastatin, pravastatin and rosuvastatin) in China from 1989 to 2019 were screened. In total, 569 clinical studies with 37 828 patients were selected from 2650 clinical trials in the database.ResultsAmong the reported cases with ADRs (2822/37 828; 7.460%), gastrointestinal symptoms were the most common (1491/37 828; 3.942%), followed by liver disease (486/37 828; 1.285%), muscle symptoms (444/37 828; 1.174%) and neurological symptoms (247/37 828; 0.653%). Pravastatin (231/1988; 11.620%) caused the most common gastrointestinal side effects, followed by fluvastatin (333/3094; 10.763%). The least likely to cause gastrointestinal irritation was rosuvastatin (82/1846; 4.442%).ConclusionIn Chinese clinical trials, gastrointestinal symptoms were the most common ADR of statin use for hyperlipidaemia and other cardiovascular diseases.