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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >An Integrated Word Embedding-Based Dual-Task Learning Method for Sentiment Analysis
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An Integrated Word Embedding-Based Dual-Task Learning Method for Sentiment Analysis

机译：基于集成词嵌入的双任务情感分析方法

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摘要

Sentiment analysis aimed to automate the task of discriminating the sentiment tendency of a textual review, which expresses asimple sentiment as positive, negative, or neutral. In general, the basic sentiment analysis solution used for feature extractionis the word embedding technique, which only focuses on the contextual or global semantic information and ignores thesentiment polarity of text. Thus, the word embedding technique leads to biased analysis results, especially for some wordsthat have the same semantic context but an opposite sentiment. In this paper, we propose an integrated sentiment embeddingmethod to combine context and sentiment information using a dual-task learning algorithm to perform sentiment analysis.First, we propose three sentiment language models by encoding the sentiment information of texts into word embedding basedon three existing semantic models, namely, continuous bag-of-words, prediction, and log-bilinear. Next, based on semanticlanguage models and the proposed sentiment language models, we propose a dual-task learning algorithm to generate hybridword embedding named integrated sentiment embedding, in which the joint learning method and parallel learning method areapplied to jointly process tasks. Experiments on sentence-level and document-level sentiment classification tasks demonstratethat the proposed integrated sentiment embedding has better classification performances compared with basicword embeddingmethods.
机译：情感分析旨在自动化区分文本评论的情感倾向的任务，该过程将简单的情感表示为积极，消极或中立。通常，用于特征提取的基本情感分析解决方案是词嵌入技术，该技术仅关注上下文或全局语义信息，而忽略文本的情感极性。因此，词嵌入技术会导致分析结果有偏差，尤其是对于某些具有相同语义上下文但情绪相反的词。本文提出了一种综合的情感嵌入方法，使用双任务学习算法将上下文和情感信息结合起来进行情感分析。首先，我们基于现有的三种语义，通过将文本的情感信息编码为单词嵌入来提出三种情感语言模型模型，即连续词袋，预测和对数双线性。接下来，基于语义语言模型和提出的情感语言模型，提出了一种用于生成混合词嵌入的双任务学习算法，称为集成情感嵌入，其中联合学习方法和并行学习方法被应用于联合处理任务。句子级和文档级情感分类任务的实验表明，与基本词嵌入方法相比，该集成情感嵌入具有更好的分类性能。

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