The rapidly increasing of sentiment analysis in social networks has lead business owners and decision makers to value opinion leaders who can influence people's impressions concerning certain business or commodity. Nevertheless, decision makers are being misled by inaccurate results due to the ignorance of perspectivism. Considering perspectivism, while computing text polarity, can help machines to reflect the human perceived sentiment within the content. This emphasises the need for integrating social behaviour (user's influence factor) with sentiment analysis (text polarity scores), providing a more pragmatic portrayal of how the writer's audience comprehend the message. In this study, a new model is proposed to intensify sentiment analysis process on Twitter. In the achievement of such, social network analysis is done using UCINET tool followed by artificial neural networks for ranking users. For sentiment classification, a hybrid approach is presented, where lexicon-based technique is combined with a fuzzy classification technique to handle language vagueness as well as for an inclusive analysis of tweets into seven classes; for the purpose of enhancing final results. The proposed model is practiced on data collected from Twitter. Results show a significant enhancement in tweets polarity scores represent more realistic sentiments.

中文翻译：

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一种基于用户排名的适用于在线社交网络的改进的模糊情感分析方法

社交网络中情感分析的迅速增长已导致企业主和决策者重视能够影响人们对某些企业或商品印象的意见领袖。然而，由于对透视主义的无知，决策者被错误的结果所误导。考虑到透视论，在计算文本极性时，可以帮助机器在内容中反映人类感知的情感。这强调了将社交行为（用户的影响因素）与情感分析（文本极性得分）相结合的必要性，从而更加务实地描绘了作者的听众如何理解信息。在这项研究中，提出了一种新模型来加强Twitter上的情绪分析过程。为了实现这一点，使用UCINET工具进行社交网络分析，然后使用人工神经网络对用户进行排名。对于情感分类，提出了一种混合方法，其中基于词典的技术与模糊分类技术相结合来处理语言的模糊性，以及将推文包含在内的七类分析。为了提高最终结果。提议的模型是根据从Twitter收集的数据实践的。结果表明，推文极性分数的显着提高代表了更为现实的情感。为了提高最终结果。提议的模型是根据从Twitter收集的数据实践的。结果表明，推文极性分数的显着提高代表了更为现实的情感。为了提高最终结果。提议的模型是根据从Twitter收集的数据实践的。结果表明，推文极性分数的显着提高代表了更为现实的情感。