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Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion

机译：基于多特征融合的评估文本的深度学习情绪分类

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摘要

Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA).To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (FINN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature.
机译：情绪分析涉及在文本中表达的意见。由于巨额评论，情感分析起到提取了重要信息和综合情感评论的基本作用。在本文中，我们提出了一种基于深度学习的方法来对评论（称为RNSA）表示的用户的意见来分类。至于我们的知识，这是一种基于深度学习的方法，其中一个代表单词的统一功能集嵌入，情感知识，情绪转移规则，统计和语言知识，尚未对情感分析进行彻底研究。 RNSA采用经常性神经网络（芬金），该网络（芬金）由长短期内存（LSTM）组成，以利用顺序处理，并以传统方法克服几个缺陷，其中订单和关于该单词的信息消失。此外，它使用情绪知识，情绪变化器规则和多种策略来克服以下缺点：具有类似语义背景但情绪极性相反的单词;情境极性;句型;单词覆盖单个词典的限制;词感变异。为了验证我们工作的有效性，我们对大型审查数据集进行句子级的情感分类。我们获得了令人鼓舞的结果。实验结果表明（1）特征向量（a）统计，语言和情感知识，（b）情绪变速器规则和（c）字嵌入可以提高句子级情绪分析的分类准确性; （2）我们从该统一功能集中学习的方法可以获得比从特征子集学习的功能的重要性; （3）我们的神经模型与文献中的其他众所周知的方法相比，卓越的性能改善。

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