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Making sense of electrical vehicle discussions using sentiment analysis on closely related news and user comments

arXiv 2112.12327 ↗First posted 23 Dec 2021cs.CL cs.AIcited by 0

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Abstract (arXiv)

We used a token-wise and document-wise sentiment analysis using both unsupervised and supervised models applied to both news and user reviews dataset. And our token-wise sentiment analysis found a statistically significant difference in sentiment between the two groups (both of which were very large N), our document-wise supervised sentiment analysis found no significant difference in sentiment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 15 references that are in the map. First posted on arXiv 23 Dec 2021.

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References · 2

  1. 2018SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentimentcited by 26
  2. 2021An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performancecited by 1

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