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Big Data Analysis in Social Networks: Extracting Food Preferences of Vegans from Twitter
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
Keyword [en]
big, data, vegan, veganism, sentiment, analysis, normalisation, normalization, profile, preferences.
National Category
Computer Science Natural Sciences
URN: urn:nbn:se:du-22460OAI: diva2:943131
Available from: 2016-06-27 Created: 2016-06-27

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