Polarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similarity Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly identifying misspelled or shortened words that are frequently encountered in text from online social media. Method performance is evaluated in two levels: i) capture rate of the misspelled and shortened words, ii) classification performance of the feature set. Our results show that classification accuracy is improved, compared to two other models in the literature, when the proposed features are used.

极性分类是情感分析中最基本的问题之一。在本文中，我们提出了一种新颖的方法，即声音余弦相似度匹配，用于Twitter消息的极性分类，该方法结合了基于音频数据而不是基于语法或其他文本属性的特征，即消除了对外部词典的依赖。它对于正确识别来自在线社交媒体的文本中经常遇到的拼写错误或缩短的单词特别有用。方法的性能分为两个级别：i）拼写错误和缩短单词的捕获率，ii）功能集的分类性能。我们的结果表明，与文献中的其他两个模型相比，使用建议的特征时，分类准确性得到了提高。