Emotion can also be measured by data? The answer of big data experts is yes.

The recently born "Southern Sina Finance Big Data Strategy Index" attempts to obtain people's "emotional judgment" on the market through non-governmental communication platforms such as Stock Bar and Weibo.

But when making decisions, emotional choices often betray data. Even if big data analysis gets a positive answer, at the last moment of making a decision, emotions will go to the opposite side of the data.

In the last game of the World Cup, Germany played against Argentina, and both teams had their own diehard fans. Miss A, a color fan, repeatedly compared the data and showed that more than 90% of the indicators indicated that Germany would win, but she still bought Argentina. For Miss A, the big data before making a decision is meaningless. She still made a decision under the guidance of her own values. This is an emotional force. Big data cannot be predicted at the last minute. For miss a, is it the love of the team in your heart or the rational judgment of winning or losing?

Big data often fails to make decisions under the cloak of emotions. If you take part in an 8-minute blind date program, network scientists can measure the social interaction, chat time, topics involved and various micro-expressions between you and six blind date partners, but they can't detect their true feelings for each other. Finally, you may ask the one with the lowest score under big data analysis. The so-called love at first sight and the sixth sense are all human "special functions" that big data can't match.

The above story reflects the limitations of data analysis. Big data analysis can help people understand the complex society, interpret its meaning and fill the cognitive gap beyond the scope of brain power, but the subtlety of human unique emotions is that data is difficult to capture and reflect. Computer data analysis is good at measuring the "quantity" rather than the "quality" of social interaction.

The advantage of big data in grasping trends is sometimes the disadvantage of this technology. If 10 children watch Happy and Big Big Wolf, does it mean that Harry Potter watched by only five people is less valuable than the former, and even The Disciples of China, which few people care about, is also a failed work? Big data analysis is good at grasping trends, but it is easy to ignore individual value differences. The result of data analysis seems objective and fair, but in fact, value choice runs through the whole process from construction to interpretation. It is difficult for big data to distinguish the meaning of value orientation in different social backgrounds.

Data don't understand narrative, so it is difficult to grasp the emergence process of thinking. A 4-or 5-year-old child can simply tell the story of Little Red Riding Hood, but data analysis can't. Children can already understand the power contrast between justice and evil in Little Red Riding Hood, which is a long-term accumulated value judgment of human society and a cultural background. But the data analysis can't understand. At least for now, big data is dominant in probability, but it can't tell a simple story.

As far as the data itself is concerned, big data analysis also has its inherent defects. Because big data analysis is based on correlation, the road of correlation between various fields has not been fully established.

Rome was not built in a day. Only when the boundaries between industries are crossed and the correlation is strengthened will the accuracy of the data be improved.

That's what Bian Xiao shared for you about what big data can't do. For more information, you can pay attention to Global Ivy and share more dry goods.