Detecting the sentiment of a utterance related to a concept, brand or groups is useful to determine the opinions of people.
simMachines recently predicted the results of an election based on Tweets and sentiment analysis. For each tweet we identify the sentiment of it.
For example, given that we focus our attention on Tweets that mention the country Costa Rica:
- Costa Rica is a beautiful: Sentiment is POSITIVE
- Costa Rica is too crowded: Sentiment is NEGATIVE
Aggregating the sentiments of many Twitter users allowed us to predict the results of the 2014 Costa Rican elections.
Sentiments can be plotted over time to obtain a high level overview of the perception of a concept:
And also a plot of concepts can be extracted based on the aggregated sentiment: