A knowledge-based methodology is proposed for sentiment analysis on social networks. The work was focused on semantic processing taking into account the content handling the public user’s opinions as excerpts of knowledge. Our approach implements knowledge graphs, similarity measures, graph theory algorithms, and a disambiguation process. The results obtained were compared with data retrieved from Twitter and users’ reviews in Amazon. We measured the efficiency of our contribution with precision, recall, and the F-measure, comparing it with the traditional method of looking up concepts in dictionaries which usually assign averages. Moreover, an analysis was carried out to find the best performance for the classification by using polarity, sentiment, and a polarity–sentiment hybrid. A study is presented for arguing the advantage of using a disambiguation process in knowledge processing. A visualization system presents the social graphs to display the sentiment information of each comment as well as the social structure and communications in the network.

中文翻译：

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基于知识的社交网络情感分析和可视化

提出了一种基于知识的方法来对社交网络进行情感分析。这项工作的重点是语义处理，考虑到将公众用户意见作为知识摘录处理的内容。我们的方法实现了知识图、相似性度量、图论算法和消歧过程。获得的结果与从 Twitter 检索的数据和亚马逊用户的评论进行了比较。我们用精度、召回率和 F 度量来衡量我们贡献的效率，并将其与在字典中查找概念的传统方法进行比较，后者通常会分配平均值。此外，通过使用极性、情绪和极性 - 情绪混合进行分析以找到分类的最佳性能。提出了一项研究来论证在知识处理中使用消歧过程的优势。可视化系统呈现社交图以显示每个评论的情感信息以及网络中的社交结构和通信。