News Analysis

This page will calculate the Mood for the news story, the page may take a while to load as this is done in realtime.

The scoring is based on the current information within the NewsMood Words Database and using the Princeton WordNet interface to provide word types and synonyms.

The analysis of the story uses scorings from the NewsMood database along with preset lists of words. Values or expressive amounts within the story are also calculated and used to multiply the effect of the surrounding keywords. As an example: one million cats died is far worse than one cat died, but probably not actually a million times worse when it comes to Mood scoring.

The word types (Noun, Verb, Adjective etc) are used to find groupings of words and this used to calculate the context of the sentances based on the position and order. Negative or inverting words such as not or didn't are also taken into consideration and the position of these words in comparison to the others can invert the whole meaning of the sentance.

The synonyms from the Princeton WordNet database of each keyword or group found in the news story are used to give a biasing word score allowing words that do not yet have a score to use the scoring of its synonyms. Each of the synonyms from the WordNet database are shown on the Word Voting page and these too can be voted on, allowing the NewsMood database to grow from the Princeton Database using the scores that are publicy voted on.