  In web era, since technology has revolutionized mankind life, plenty of data
and information are published on the Internet each day. For instance, news
agencies publish news on their websites all over the world. These raw data
could be an important resource for knowledge extraction. These shared data
contain emotions (i.e., positive, neutral or negative) toward various topics;
therefore, sentimental content extraction could be a beneficial task in many
aspects. Extracting the sentiment of news illustrates highly valuable
information about the events over a period of time, the viewpoint of a media or
news agency to these events. In this paper an attempt is made to propose an
approach for news analysis and extracting useful knowledge from them. Firstly,
we attempt to extract a noise robust sentiment of news documents; therefore,
the news associated to six countries: United State, United Kingdom, Germany,
Canada, France and Australia in 5 different news categories: Politics, Sports,
Business, Entertainment and Technology are downloaded. In this paper we compare
the condition of different countries in each 5 news topics based on the
extracted sentiments and emotional contents in news documents. Moreover, we
propose an approach to reduce the bulky news data to extract the hottest topics
and news titles as a knowledge. Eventually, we generate a word model to map
each word to a fixed-size vector by Word2Vec in order to understand the
relations between words in our collected news database.
