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首页> 外文期刊>Concurrency and computation: practice and experience >Word2Sent: A new learning sentiment-embeddingmodel with low dimension for sentence level sentiment classification
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Word2Sent: A new learning sentiment-embeddingmodel with low dimension for sentence level sentiment classification

机译：Word2sent：一个新的学习情绪 - 嵌入式模型，具有低维度的句子级情绪分类

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摘要

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.
机译：Word Embedding模型成为越来越重要的方法，将单词嵌入到高维空间中。这些模型已被广泛利用，以提取语义和句法特征进行情绪分析。但是，使用Word Embedding模型不能足以进行情感分析任务，因为它们不包含情绪功能。因此，嵌入模型的单词无法充分满足依赖识别句子极性的情绪分析应用的综合需求。在本文中，我们提出了一种情绪嵌入模型（Word2sent模型）来解决现有词嵌入模型的弱点，用于情感分析应用程序。我们基于连续的单词模型和SentiwordNet Lexicon开发了该模型，从其周围的上下文词中学习为每个单词嵌入的情绪嵌入。它保留了语义和语法特征，并捕获隐式的情绪。此外，它可以预测比传统方式非常低的情绪嵌入嵌套尺寸的情绪特征。该方法提供了改进的情绪分类性能，并降低了计算复杂性。获得的精度性能和处理时间结果表明，所提出的模型尤为前景。

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