Analyzing users' sentiment towards popular consumer industries and brands on Twitter
September 21, 2017 ยท Declared Dead ยท ๐ 2017 IEEE International Conference on Data Mining Workshops (ICDMW)
"No code URL or promise found in abstract"
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Authors
Guoning Hu, Preeti Bhargava, Saul Fuhrmann, Sarah Ellinger, Nemanja Spasojevic
arXiv ID
1709.07434
Category
cs.CL: Computation & Language
Cross-listed
cs.IR,
cs.SI
Citations
29
Venue
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Last Checked
2 months ago
Abstract
Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12,898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month. We find that users tend to be most positive towards manufacturing and most negative towards service industries. In addition, they tend to be more positive or negative when interacting with brands than generally on Twitter. We also find that sentiment towards brands within an industry varies greatly and we demonstrate this using two industries as use cases. In addition, we discover that there is no strong correlation between topic sentiments of different industries, demonstrating that topic sentiments are highly dependent on the context of the industry that they are mentioned in. We demonstrate the value of such an analysis in order to assess the impact of brands on social media. We hope that this initial study will prove valuable for both researchers and companies in understanding users' perception of industries, brands and associated topics and encourage more research in this field.
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