A thorough analysis of people’s sentiment about a business, an event or an individual is necessary for business development, event analysis and popularity assessment. Social networks are rich sources of obtaining user opinions about people, events and products. Sentiment analysis conducted using multiple user comments and messages on microblogs is an interesting field of data mining and natural language processing (NLP). Different techniques and algorithms have recently been developed for conducting sentiment analysis on Twitter. Different proposed classification and pure NLP-based methods have different behaviours in predicting sentiment orientation. In this study, we combined the results of the classic classifiers and NLP-based methods to propose a new approach for Twitter sentiment analysis. The proposed method uses a fuzzy measure for determining the importance of each classifier to make the final decision. Fuzzy measures are used with the Choquet fuzzy integral for fusing the classifier outputs in order to generate the final label. Our experiments with different Twitter sentiment datasets show that fuzzy integral-based classifier fusion improves the average accuracy of sentiment classification.

中文翻译：

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基于模糊积分分类器融合的推特情感分析

彻底分析人们对企业、事件或个人的情绪对于企业发展、事件分析和受欢迎程度评估是必要的。社交网络是获取用户对人物、事件和产品意见的丰富来源。使用微博上的多个用户评论和消息进行情感分析是数据挖掘和自然语言处理 (NLP) 的一个有趣领域。最近开发了不同的技术和算法来在 Twitter 上进行情感分析。不同的提议分类和纯基于 NLP 的方法在预测情感方向方面具有不同的行为。在这项研究中，我们结合了经典分类器和基于 NLP 的方法的结果，提出了一种新的 Twitter 情感分析方法。所提出的方法使用模糊度量来确定每个分类器的重要性以做出最终决策。模糊度量与 Choquet 模糊积分一起用于融合分类器输出以生成最终标签。我们对不同 Twitter 情感数据集的实验表明，基于模糊积分的分类器融合提高了情感分类的平均准确度。