Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
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.
big, data, vegan, veganism, sentiment, analysis, normalisation, normalization, profile, preferences.