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Sentiment Learning Using Twitter Ideograms

机译：使用Twitter Idefogs的情感学习

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摘要

It has been extremely important to know what the audience feels. With the growing technologies, people get to know what the majority thinks about a particular subject through review sites, people opinion voting sites, campaigns. Thus, it becomes necessary to get a conclusion on this so that the opinion-affected organization or individual gets a clear idea and works further. This survey introduces techniques and approaches that would directly help information-gathering systems. The present study focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in a more traditional-approach. Hence, we have tried to make the system more accurate by analyzing symbols which play a major role in sentiment learning. Proposed algorithm is compared and findings are presented in that explains each algorithm and its use in mining and analysis of Twitter textual data and provides deep insights as to what is the accuracy level in the mining process. We include a brief summary on broader issues such as privacy, manipulation and other related issues that opinion-access services gives rise to.
机译：知道观众感受到的是非常重要的。随着越来越多的技术，人们通过审查网站，人们意见投票网站，竞选方式了解大多数人对特定主题的了解。因此，有必要获得此结论，以便意见 - 受影响的组织或个人获得明确的想法并进一步处理。本调查介绍了直接帮助信息收集系统的技术和方法。本研究侧重于寻求解决情绪感知应用程序提出的新挑战的方法，与已经以更传统的方法存在的方式相比。因此，我们试图通过分析在情感学习中发挥重要作用的符号来使系统更准确。比较了算法，并提出了发现的结果，解释了每个算法及其在Twitter文本数据的挖掘和分析中的使用，并为挖掘过程中的准确度水平提供了深入的见解。我们简要概述了更广泛的问题，如隐私，操纵和其他相关问题，即意见访问服务产生的影响。

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