Consider a news headline posted on Twitter: Rupee soars & profit of Infosys dips. Ask a machine to ‘read’ the news and ‘tell’ us what the news is all about. What appears to be a simple matter of reading for a human may get quite difficult for a machine unless it is ‘trained’ properly.
The headline includes two nouns (‘rupee’ and ‘Infosys’) and two verbs (‘soars’ and ‘dips’). If the machine reads the verbs in the wrong places with respect to the nouns, the meaning of the headline completely changes. Thus, the algorithm should be developed in such way that a machine ‘reads’ and ‘understands’ the story in the same way a human does. This would involve several things that include associating every verb and adjective with the correct noun.
Welcome to the field of news analytics, tools that convert a text into sentiment score. It is known that stock markets worldwide are influenced by sentiments more than fundamental factors, and regular and real-time news shapes market sentiments. A discipline that measures the relevance, sentiment and novelty of news has emerged. This is news analytics.
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