Web sources such as social networks, internet forums, and user reviews, provide large amounts of unstructured text data. Due to the steady development of new platforms and the increasing number of internet users, the interest in methods that automatically extract discussed topics in text data has increased in recent years. Organizations and scholars from different fields can utilize such methods to identify patterns and generate new insights. Examples are opinion researchers investigating current opinions on political and societal issues, consumer researchers interested in consumer beliefs about the consumption and production of goods, and marketing managers curious about the public perception of their products and services.<\/p>\n
Existing unsupervised methods to detect topics such as clustering and topic modeling represent topics as clusters or word lists (e.g., Blei et al., 2003; Chen et al., 2014). This information is often insufficient to get a comprehensible topic overview because words can be used in different contexts. Moreover, resulting word lists are often incoherent and consist of loosely related words. Additional information, like representative sentences, initial topic labels, and topic correlations, is necessary to gain a deeper understanding. Therefore, I developed a new user-friendly visualization and labeling toolkit called SocialVisTUM as part of my master\u2019s thesis. SocialVisTUM enables users to get a quick and comprehensible visual overview of topics and topic relations for any English text corpus and can be accessed online.<\/a> Moreover, a variety of features can be used to customize the visualization and change initial topic labels. To exemplify the usage and show one possible use case, let\u2019s test it on a new data set that consists of user comments about organic food. We will use a total of 83.938 sentences from the comment sections of articles about organic food from news websites like The Washington Post and the New York Times.<\/p>\n <\/p>\n<\/div>