Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning
Yujiao Li, Hasan A. Fleyeh · 2018
This paper studied public emotion and opinion concerning the opening of new IKEA stores, specifically, how much attention are attracted, how much positive and negative emotion are aroused, what IKEA-related topics are talked due to this event. Emotion is difficult to measure in retail due to data availability and limited quantitative tools. Twitter texts, written by the public to express their opinion concerning this event, are used as a suitable data source to implement sentiment analysis. Around IKEA opening days, local people post IKEA related tweets to express their emotion and opinions on that. Such “IKEA” contained tweets are collected for opinion mining in this work. To compute sentiment polarity of tweets, lexicon-based approach is used for English tweets, and machine learning methods for Swedish tweets. The conclusion is new IKEA store are paid much attention indicated by significant increasing tweets frequency, most of them are positive emotions, and four studied cities have different topics and interests related IKEA. This paper extends knowledge of consumption emotion studies of prepurchase, provide empirical analysis of IKEA entry effect on emotion. Moreover, it develops a Swedish sentiment prediction model, elastic net method, to compute Swedish tweets' sentiment polarity which has been rarely conducted.