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The role of Entity - level news sentiment in enhancing the understanding of current events with The opinions, emotions, or attitudes expressed in the news can have a significant impact on customer Newsdata.io analysis tool to get the understanding of your market.
And investor behavior. As a result, brands and organizations, for example, may want to know how people feel about a specific product or an upcoming event. They may also want to look for any links between an article's vitality and the various entity sentiments in it. Financial institutions can also use news sentiment to forecast market movements in the future.
So, how can businesses best measure news sentiment in order to gain these types of insights?
A Quick Overview of News Sentiment Measuring
Typically, news sentiment analysis is measured on two levels:
· Document-level sentiment – the overall sentiment of a news article
· entity-level sentiment – the emotion associated with an entity (i.e., a person, place, or thing) in a news article.
The Benefits of Entity-Level News Sentiment
A holistic view of an article's news sentiment isn't always sufficient. An article may be overall positive, but the majority of the entities mentioned may have a negative connotation. The sentiment at the article level can have an impact on the sentiment at the entity level. Different entities can have widely disparate emotions.
Insights into the Tokyo Olympics
Athletes selected for the United States Olympic basketball and wrestling teams had the highest entity sentiment scores, averaging around 80%. Male wrestlers had slightly higher scores than female wrestlers, but not by much.
Would there be a significant difference if we compared the number of times the words "male" and "female" appeared in the various articles and their corresponding sentiment? Is this difference due to a preference for male athletes over female athletes?
Another interesting finding was that soccer players who qualified for the US team had lower positive sentiment scores ranging from 58% to 70%. Can we link this finding to how Americans perceive soccer as a sport? Do more experience athletes, who are also more likely to be mentioned in the media, have a higher positive sentiment in general?
Finally, if we drill down by author or reporter of different articles, will we see that articles by the same reporter on a specific sport or about specific athletes have a higher or lower sentiment? Is this sentiment representative of the general public's perception of those sports or athletes?
These are the kinds of insights that entity-level sentiment can provide.
Accurate News Sentiment Analysis Starts with High-Quality Data
At the entity level, news sentiment analysis provides a more granular perspective. This is especially useful when analyzing thousands of articles and entities to identify patterns and trends. These insights can then be applied to financial institutions' predictive models. Brands can begin to distinguish between changes in brand sentiment and changes in product sentiment. Organizations that conduct risk intelligence can detect negative media more easily.
Newsdata.io's Enriched API can provide these organizations with high-quality news sentiment analysis at both the article and entity levels.
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