Sentiment analysis or opinion mining for subject information extraction from the text has become more and more dependent on natural language processing, especially for business and healthcare, since the online products and service reviews affect the consuming behaviors. Word embeddings that can map the words to low-dimensional vector representations have been widely used in natural language processing tasks. But the word embeddings based on context such as Word2Vec and GloVe fail to capture the sentiment information. Most of existing sentiment analysis methods incorporate emotional polarity (positive and negative) to improve the sentiment embeddings for the emotion classification. This article takes advantage of an emotional psychology model to learn the emotional embeddings in Chinese first. In order to combine the semantic space and an emotional space, we present two different purifying models from local (LPM) and global (GPM) perspectives based on Plutchik's wheel of emotions to add the emotional information into word vectors. The two models aim to improve the word vectors so that not only the semantically similar words but also the sentimentally similar words can be closer than before. The Plutchik's wheel of emotions model can give eight-dimensional vector for one word in emotional space that can capture more sentiment information than the binary polarity labels. The obvious advantage of the local purifying model is that it can be fit for any pretrained word embeddings. For the global purifying model, we can get the final emotional embeddings at once. These models have been extended to handle English texts. The experimental results on Chinese and English datasets show that our purifying model can improve the conventional word embeddings and some proposed sentiment embeddings for sentiment classification and multi-emotion classification.

中文翻译：

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Emo2Vec

从文本中提取主题信息的情感分析或意见挖掘越来越依赖于自然语言处理，尤其是在商业和医疗保健领域，因为在线产品和服务评论会影响消费行为。可以将单词映射到低维向量表示的词嵌入已广泛用于自然语言处理任务。但是 Word2Vec 和 GloVe 等基于上下文的词嵌入无法捕获情感信息。大多数现有的情感分析方法都结合了情感极性（正面和负面）来改进情感分类的情感嵌入。本文首先利用情绪心理学模型来学习中文的情绪嵌入。为了结合语义空间和情感空间，我们基于 Plutchik 的情感轮从局部（LPM）和全局（GPM）的角度提出了两种不同的净化模型，将情感信息添加到词向量中。这两个模型旨在改进词向量，以便不仅语义相似的词，而且情感相似的词都可以比以前更接近。Plutchik 的情感轮模型可以为情感空间中的一个词提供八维向量，可以捕获比二元极性标签更多的情感信息。局部净化模型的明显优势是它可以适用于任何预训练的词嵌入。对于全局净化模型，我们可以一次得到最终的情感嵌入。这些模型已扩展到处理英文文本。