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.

翻译：人们广泛使用万维网分享他们对产品、服务或旅行目的地等实体的体验。以评论和意见形式提供的在线反馈文本对消费者决策至关重要。这些评论构成了可衡量产品或服务满意度的宝贵资源。情感分析是一项识别此类文本片段中所表达观点的任务。本文我们开发了两种方法，结合不同类型的词向量来学习和估计评论的情感极性。我们从词向量构建平均评论向量，并在正向和负向灵敏度标注评论中利用词频为这些评论向量添加权重。我们将这些方法应用于来自不同领域的多个数据集，这些数据集被用作情感分析的标准基准。我们将这些技术彼此及现有方法进行集成，并与文献中的方法进行比较。结果表明，我们的方法性能优于当前最优的成功率。