Name
Abstract | ||
|---|---|---|
In online social media, individuals produce vast amounts of content which in effect "instruments" the world around us. Users on sites such as Twitter are publicly broadcasting status updates that provide an indication of their mood at a given moment in time, often accompanied by geolocation information. A number of strategies exist to aggregate such content to produce sentiment scores in order to build a "happiness index". In this paper, we describe such a system based on Twitter that maintains a happiness index for nine US cities. The main contribution of this paper is a companion system called Sentire Crowds that allows us to identify the underlying causes behind shifts in sentiment. This ability to analyze the components of the sentiment signal highlights a number of problems. It shows that sentiment scoring on social media data without considering context is difficult. More importantly, it highlights cases where sentiment scoring methods are susceptible to unexpected shifts due to noise and trending memes. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDMW.2011.61 | Data Mining Workshops |
Keywords | Field | DocType |
sentiment score,main contribution,us city,sentire crowds,geolocation information,national happiness indices,social media data,sentiment signal,online social media,deriving insights,companion system,happiness index,sentiment analysis,visualisation,data mining,social media,internet,indexation,social network analysis,visualization | Crowds,Data mining,Broadcasting,Social media,Computer science,Sentiment analysis,Social network analysis,Geolocation,Happiness,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-4673-0005-6 | 9 | 0.63 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anthony Brew | 1 | 73 | 4.77 |
| Derek Greene | 2 | 489 | 24.55 |
| Daniel Archambault | 3 | 705 | 39.10 |
| Pádraig Cunningham | 4 | 3086 | 218.37 |