Field report from sediment coring in peat for landscape analysis in Hjaltadalur, Skagafjörður, north Iceland 2010 : Sediment drilling in Hólar, Vidvik and Kolkuós, Hjaltadalur

This field report presents fieldwork undertaken in Hjaltadalur, Skagafjördur, northern Iceland during summer 2010. The main aim was to initiate coring in selected mires in order to determine the composition of organic material and sediments in the mires, sub-sample for sedimentological and palaeoecological analyses, and initiate advanced landscape analysis of Hjaltadalur. Three mires were selected for sediments coring in Hjaltadalur: Ástunga close to Kolkóus, Hólakot at Viðvik, and Hólar. All three represented a landscape transect in NW to SE direction, from close to the coast to valley interior, i.e. close to the old settlement at Hólar.

Big Data Analysis in Social Networks : Extracting Food Preferences of Vegans from Twitter

Market research is often conducted through conventional methods such as surveys, focus groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming. This study develops a new method, based on a combination of standard techniques like sentiment analysis and normalisation, to conduct market research in a manner that is free and quick. The method can be used in many application-areas, but this study focuses mainly on the veganism market to identify vegan food preferences in the form of a profile. Several food words are identified, along with their distribution between positive and negative sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and chicken dominate the profile, indicating that there is a significant market for vegan-suitable alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato, blueberries, kale and tofu also make strong appearances in the profile. Validation is performed by using the method on Volkswagen vehicle data to identify positive and negative sentiment across five car models. Some results were found to be consistent with sales figures and expert reviews, while others were inconsistent. The reliability of the method is therefore questionable, so the results should be used with caution.