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Sentiment Endpoints

Data for sentimental analysis and decision making

Endpoints

get
https://api.wisdomise.io/v0
/insights/twitter-emotions/
Retrieves The Twitter Emotion Insights
get
https://api.wisdomise.io/v0
/insights/twitter-sentiments/
Retrieves The Twitter Sentiment Insights
get
https://api.wisdomise.io/v0
/insights/twitter-frequencies/
Retrieves The Twitter Bull/Bear Frequencies Insights
get
https://api.wisdomise.io/v1
/insights/sentiment/word-connect
Word Connect (Available upon request)
get
https://api.wisdomise.io/v1
/insights/sentiment/word-cloud
Word Cloud (Available upon request)

Use Cases

Twitter

To understand the crypto market better, one must understand how different participants feel about various events; for instance, Elon Musk's tweets can have a significant effect on the market. There is no doubt that Twitter is the main battleground for different individuals regarding the status of the market. We can make better decisions with clean and clear insights from sentimental analysis.
As it is obvious, Twitter is the main battlefield of different people regarding the status of the market. Having clean and clarified insights from the sentimental analysis can help us with better decision-making.
All the mentioned models can be considered as input for another analyzer model or could be used as a gate for taking a decision.

Twitter Models Performance

  • The F1 score for the sentiment classification model is about 72.9%.
  • The F1 score for the emotion classification model is about 77.3 %.
  • The Bull/Bear frequency analyzer model just considers related keywords for proportion calculation.

Word Connect

Indicates the relation between different nodes regarding the keywords being searched and queried around the web. By having this feature, overall sentiment of users around of the industry can be mined.

Word Cloud

Presents the frequency of usage of different words based on each coin. For illustration, you can find out that mining keyword is more popular for Bitcoin vs nft.
Understanding different users' tendencies based on the country and state can be really helpful for clustering web3 clients. In addition, it indicates the popularity of specific keywords as well.