In the last few years, online reviews where individuals express their thoughts, interests, experiences, and opinions have broadly spread over the internet. Sentiment analysis has evolved to analyze these online reviews and provide valuable insights for both individuals and organizations that may help them in making decisions. Unfortunately the performance of sentiment analysis process is affected by the nature of online reviews' content that may contain emoticons and negation words. Moreover, spam reviews have been written for the purpose of deceiving others. Therefore, there is a need to develop an approach that considers these issues. In this paper, an enhanced approach for sentiment analysis is proposed which aims to enhance the performance of classifying reviews based on their features and assigning accurate sentiment score to features. This enhanced approach is achieved by handling negation, detecting emoticons, and detecting spam reviews using a combination of different types of properties which leads to achieving better predictive performance. The proposed approach has been verified against three datasets of different sizes. The results indicate that the proposed approach achieves a maximum accuracy of about 99.06% in detecting spam reviews and a maximum accuracy of about 97.13% in classifying reviews.

中文翻译：

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增强的基于特征的情感分析方法

在过去的几年中，人们表达自己的想法，兴趣，经验和观点的在线评论已广泛传播到互联网上。情感分析已经发展成为可以分析这些在线评论，并为个人和组织提供有价值的见解，从而可以帮助他们做出决策。不幸的是，情感分析过程的执行受到在线评论内容的性质的影响，该评论可能包含表情符号和否定词。此外，撰写垃圾邮件评论是为了欺骗他人。因此，需要开发一种考虑这些问题的方法。在本文中，提出了一种增强的情感分析方法，旨在增强基于评论的特征对评论进行分类并为特征分配准确的情感评分的性能。通过使用各种类型的属性的组合来处理否定，检测表情符号和检测垃圾邮件评论，从而实现更好的预测性能，从而实现了这种增强的方法。所提出的方法已经针对三个不同大小的数据集进行了验证。结果表明，该方法在检测垃圾邮件评论中达到了约99.06％的最大准确性，在分类评论中达到了约97.13％的最大准确性。