论文标题
使用平均加权单词矢量特征的情感分析
Sentiment Analysis Using Averaged Weighted Word Vector Features
论文作者
论文摘要
人们大量使用万维网与产品，服务或旅行目的地等实体分享自己的经验。以评论和评论的形式提供在线反馈的文本对于做出消费者的决策至关重要。这些评论创造了一个有价值的来源，可用于衡量与产品或服务相关的满意度。情感分析是确定在此类文本片段中表达的意见的任务。在这项工作中，我们开发了两种方法，它们结合了不同类型的单词向量来学习和估计评论的极性。我们使用单词向量开发了平均评论向量，并使用正面和负灵敏度标记的评论中的单词频率增加了此评论向量的权重。我们将方法应用于来自不同域的几个数据集，这些数据集用作情感分析的标准基准。我们将技术彼此和现有方法结合在一起，并与文献中的方法进行了比较。结果表明，我们方法的性能优于最先进的成功率。
People use the world wide web heavily to share their experience with entities such as products, services, or travel destinations. Texts that provide online feedback in the form of reviews and comments are essential to make consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate polarity of reviews. We develop average review vectors from word vectors and add weights to this review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains that are used as standard benchmarks for sentiment analysis. We ensemble the techniques with each other and existing methods, and we make a comparison with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.
