Social media platforms, thanks to their inherent nature of quick and far-reaching dissemination of information, have gradually supplanted the conventional media and become the new loci of political communication. These platforms not only ease and expedite communication among crowds, but also provide researchers huge and easily accessible information. This huge information pool, if it is processed with a systematic analysis, can be a fruitful data source for researchers. Systematic analysis of data from social media, however, poses various challenges for political analysis. Significant advances in automated textual analysis have tried to address such challenges of social media data. This paper introduces one such novel technique to assist researchers doing textual analysis on Twitter. More specifically, we develop a clustering methodology based on Longest Common Subsequence Similarity Metric, which automatically groups tweets with similar content. To illustrate the usefulness of this technique, we present some of our findings from a project we conducted on Turkish sentiments on Twitter towards Syrian refugees
