This paper presents a state-of-the-art approach for sentiment polarity classification. Our approach relies on an ensemble of Bidirectional Long Short-Term Memory networks equipped with a neural attention mechanism. The system makes use of pre-trained word embeddings, and is capable of predicting new vectors for out-of-vocabulary words, by learning distributional representations based on word spellings. Also, during the training process the recurrent neural network is used to perform a fine-tuning of the original word embeddings, taking into account information about sentiment polarity. This step can be particularly helpful for sentiment analysis, as word embeddings are usually built based on context information, while words with opposite sentiment polarity often occur in similar contexts. The system described in this paper is an improved version of an approach that competed in a recent challenge on semantic sentiment analysis. We evaluate the performance of the system on the same multi-domain test set used by the organizers of the challenge, showing that our approach allows reaching better results with respect to the previous top-scoring system. Last but not least, we embedded the proposed sentiment polarity approach on top of a humanoid robot to lively identify the sentiment of the speaking user.

本文提出了一种用于情感极性分类的最新方法。我们的方法依赖于配备有神经注意机制的双向长期短期记忆网络。该系统利用预训练的单词嵌入，并且能够通过学习基于单词拼写的分布表示来预测词汇外单词的新矢量。同样，在训练过程中，考虑到有关情感极性的信息，使用递归神经网络对原始单词嵌入进行微调。此步骤对于情感分析特别有用，因为单词嵌入通常基于上下文信息构建，而具有相反情感极性的单词通常出现在相似的上下文中。本文描述的系统是该方法的改进版本，可与语义情感分析的最新挑战竞争。我们在挑战组织者使用的同一多域测试集上评估了系统的性能，表明我们的方法相对于以前的最高评分系统可以达到更好的结果。最后但并非最不重要的一点是，我们将拟议的情感极性方法嵌入人形机器人的顶部，以生动地识别说话用户的情感。