Abstract
In this technological era, we have seen a huge increase in the number of reviewing sites in the internet. In case of online food delivery stores, these reviews are very important as they, on the whole express public sentiment towards a particular restaurant or cuisine. In this paper, we are proposing an approach to predict which cuisines and restaurants are “trending” in a country based on the analysis of social media. We mine social media platforms like Twitter for food-related tweets and extract these tweets by using our own manually curated food lexicon. From these tweets, we use similarity matching to extract the food items that were tweeted about and run each of these items through a cuisine classifier based on logistic regression and word2vec word embeddings. This is done for all the tweets and thus, we can get which cuisines and restaurants have been popular while, which restaurants are fading. Our approach can, therefore be used by restaurants to analyze which markets they need to expand into and also where they have to revamp their business strategies.
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The authors would like to thank the reviewers and the experts who have helped in this research and given us great insights and thoughts which have guided us and helped us improve our work.
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Lokeshkumar, R., Sabnis, O.V., Bhattacharyya, S. (2020). A Novel Approach to Extract and Analyse Trending Cuisines on Social Media. In: Pandian, A., Palanisamy, R., Ntalianis, K. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_72
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DOI: https://doi.org/10.1007/978-3-030-43192-1_72
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