Word embedding models become an increasingly important method that embeds words into a high dimensional space. These models have been widely utilized to extract semantic and syntactic features for sentiment analysis. However, using word embedding models cannot be sufficient for sentiment analysis tasks because they do not contain sentiment features. Therefore, word embedding models do not adequately meet the comprehensive needs of sentiment analysis applications that rely on recognizing the polarity of a sentence. In this paper, we propose a sentiment embedding model (Word2Sent model) to tackle the weaknesses of the existing word embedding models for sentiment analysis applications. We developed this model based on the Continuous Bag‐of‐Words model and SentiWordNet lexicon to learn sentiment embedding for each word from its surrounding context words. It preserves semantic and syntactic features and captures implicitly sentiment ones. Besides, it can predict sentiment features in a very low sentiment embeddings dimension than traditional ones. The proposed method provides an improved sentiment classification performance and lowers the computational complexity. Both the accuracy performance and processing time results obtained indicate that the proposed model is particularly promising.

中文翻译：

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Word2Sent：一种新的低维学习情感嵌入模型，用于句子级情感分类

词嵌入模型成为一种将词嵌入到高维空间中的日益重要的方法。这些模型已被广泛用于提取语义和句法特征以进行情感分析。但是，使用词嵌入模型不足以完成情感分析任务，因为它们不包含情感特征。因此，单词嵌入模型不能充分满足依赖于识别句子极性的情感分析应用程序的全面需求。在本文中，我们提出了一种情感嵌入模型（Word2Sent模型），以解决现有的用于情感分析应用的单词嵌入模型的弱点。我们基于连续词袋模型和SentiWordNet词典开发了此模型，以从其周围的上下文词中学习每个词的情感嵌入。它保留了语义和句法特征，并隐含了情感特征。此外，它可以在比传统嵌入技术低得多的情感嵌入维度中预测情感特征。所提出的方法提供了改进的情感分类性能并降低了计算复杂度。所获得的精度性能和处理时间结果均表明，所提出的模型特别有希望。所提出的方法提供了改进的情感分类性能并降低了计算复杂度。所获得的精度性能和处理时间结果均表明，所提出的模型特别有希望。所提出的方法提供了改进的情感分类性能并降低了计算复杂度。所获得的精度性能和处理时间结果均表明，所提出的模型特别有希望。