Impact of Anonymization on Sentiment Analysis of Twitter Postings
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The process of policy-modelling, and the overall field of policy-making are complex and put decision-makers in front of great challenges. One of them is present in form of including citizens into the decision-making process. This can be done via various forms of E-Participation, with active/passive citizen-sourcing as one way to tap into current discussions about topics and issues of relevance towards the general public. An increased understanding of feelings behind certain topics and the resulting behavior of citizens can provide great insight for public administrations. Yet at the same time, it is more important than ever to respect the privacy of the citizens, act in a legally compliant way, and therefore foster public trust. While the introduction of anonymization in order to guarantee privacy preservation represents a proper solution towards the challenges stated before, it is still unclear, if and to what extent the anonymization of data will impact current data analytics technologies. Thus, this research paper investigates the impact of anonymization on sentiment analysis of social media, in the context of smart governance. Three anonymization algorithms are tested on Twitter data and the results are analyzed regarding changes within the resulting sentiment. The results reveal that the proposed anonymization approaches indeed have a measurable impact on the sentiment analysis, up to a point, where results become potentially problematic for further use within the policy-modelling domain.
Index Termssentiment analysis social media anonymization re-identification policy-modelling
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