Computer Science > Social and Information Networks
Topic and Sentiment Analysis on OSNs: a Case Study of Advertising Strategies on Twitter
(Submitted on 23 Dec 2013)
Abstract: Social media have substantially altered the way brands and businesses
advertise: Online Social Networks provide brands with more versatile and
dynamic channels for advertisement than traditional media (e.g., TV and radio).
Levels of engagement in such media are usually measured in terms of content
adoption (e.g., likes and retweets) and sentiment, around a given topic.
However, sentiment analysis and topic identification are both non-trivial
In this paper, using data collected from Twitter as a case study, we analyze
how engagement and sentiment in promoted content spread over a 10-day period.
We find that promoted tweets lead to higher positive sentiment than promoted
trends; although promoted trends pay off in response volume. We observe that
levels of engagement for the brand and promoted content are highest on the
first day of the campaign, and fall considerably thereafter. However, we show
that these insights depend on the use of robust machine learning and natural
language processing techniques to gather focused, relevant datasets, and to
accurately gauge sentiment, rather than relying on the simple keyword- or
frequency-based metrics sometimes used in social media research.
Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph)
(or arXiv:1312.6635v1 [cs.SI] for this version)
From: Hamed Haddadi [
Mon, 23 Dec 2013 18:32:06 GMT (386kb,D)