First talk on CSN11 – Sentiment analysis

According to the Condorcet theorem, bigger crowds do better. If each individual independently decides with the same probability, the collective reaches ‘the right’ decision fast. If the individuals can connect, the collective behavior can become very rich and complex. See Linux, wikipedia.

But on social networks, there is no collective ‘goal’ besides connecting, recreation. Still, the chatter can predict the box office receipts. And Google search terms shows where people have flu, better than any medical database. It is also shown that happiness and loneliness is contagious; if many people in your network are lonely, you feel more lonely too.

His research focuses on sentiment, by extracting indicators from text. And predicting the future from that. This can work well, if applied on large numbers, see and Facebook gross happiness index.

McNair defines 72 terms, that map to 6 independent dimensions. These terms are applied to tweet texts, resulting in 6 charts. It turns out that about 8% of all tweets show emotional content. Charts of Election08 shows dips and peaks that make sense; energetic on election day, for example.

The Dow Jones industrial average appears to correlate with the Calm measurement, but with a lag of about 4-5 days. It can be used to predict its future value, with an accuracy of 86%, whatever that may mean.. See for his paper.

He also studied mood contagion. Look at twitter users who follow each other, and then the use of emoticons. People follow others with similar traits, assortative network. He shows that sad people connect to sad people, and happy to happy people. His recommendation is to connect to happy people, and to disconnect from unhappy ones. 🙂

He concludes with the question if we can unleash a happiness virus on the network, would that work? Or push another mood?

Nice talk by The slides of his presentation can be found at


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