Sentiment Analysis of Twitter Data: Towards Filtering, Analyzing and Interpreting Social Network Data

Abstract

Social networks provide a rich data source for researchers that can be accessed in a comparatively effortless way. As data and text mining methods such as Sentiment Analysis are becoming increasingly refined, the wealth of social network data opens up entirely new possibilities for exploring specific in-depth research questions. In this paper an approach towards the retrieval, analysis and interpretation of social network data for research purposes is developed. The data is filtered according to relevant criteria and analyzed using Sentiment Analysis tools tailored specifically to the data source. The approach is verified by applying it to two example research questions, confirming past findings on cultural and gender differences in sentiment expression.