Classification of users

[audrism]

[audrism]

Integrating d2v to classify users
learning to add RF on top

[jball1997]

Good results with doc2vec. Haven't integrated with RF yet

[jball1997]

Our doc2vec results are actually pretty confusing. The accuracies are good at labeling a user correctly but we also get a lot of false positives. The model is terrible when we feed it custom text. We think this is an issue with the sparsity of our data. We will look into this.

[mickidymick]

Added more users that were not relevant to help even out the data.
Classified the users in Users_Labeled using only the user bio gives a total Fscore of 93.8%.
Added the last 5 tweets for each user.
Classified the users in Users_Labeled using only the tweets gives a total Fscore of 96.5%
Classified the users in Users_Labeled using both the tweets and bios gives a total Fscore 97.5%

[jball1997]

We've increased our dataset by adding irma users. We are working on increasing our data in general also

[jball1997]

10 FOLD VERIFICATION ON CURRENT TRAINING SET

Labeling only tweets:
government: 97%
news: 97%
not_news: 96%
nonprofits: 97%
utility: 95%

Labeling only bio:
government: 94%
news: 94%
not_news: 93%
nonprofits: 93%
utility: 93%

Labeling both bios and tweets with RandomForest:
government: not finished
news: not finished
not_news: not finished
nonprofits: not finished
utility: not finished

Labeling both bios and tweets with Doc2Vec:
government: 97%
news: 97%
not_news: 97%
nonprofits: 96%
utility: 97%