Words and language have connotations and positive or negative sentiments. The phrase "The candidate faltered" has a negative connotation simply from the word "faltered". The phrase "The candidate's popularity skyrocketed" holds positive sentiment based on the phrase "popularity skyrocketed". The phrase "The candidate's ideas remained steady" is slightly different from "The candidate's ideas remained steadfast". All of these sentences have varying degrees of positivity or negativity.
Our algorithms look at the wording of each piece of content and decide an overall score, which we display to you. You can then use this score however you see fit. This applies to news stories as well as tweets.
Our sentiment score is based on a range of -100% to 100%, where the lower the score, the less positive it is. A score around zero means the content is relatively neutral, while a score such as -87% would be very negative.
Some use cases include:
- scanning for positive or negative trends in topics
- reading news contrary to your investment goals to cover all possible weaknesses
- jumping into investments with a strong sentiment, positive or negative
- making educated guesses about news without reading the full article
We're sure our users can think of other ways to use sentiment analysis. We just make the software to make your decisions easier.