Opinion Mapping Travelblogs

User-contributed content represents a valuable information source provided one can make sense of the large amounts of unstructured data. This work focusses on geospatial content and specifically on travelblogs. Users writing stories about their trips and related experiences effectively provide geospatial information albeit in narrative form. Identifying this geospatial aspect of the texts by means of applying information extraction techniques and geocoding, we relate portions of texts to locations, e.g., a paragraph is associated with a spatial bounding box. To further summarize the information, we assess the opinion (“mood”) of the author in the text. Aggregating this mood information for places, we essentially create a geospatial opinion map based on the user-contributed information contained in the articles of travelblogs. We assessed the proposed approach with a corpus of more than 150k texts from various sites.