Towards an On-Line Analysis of Tweets Processing

to the right. When the dogs had adjusted themselves to this position, the box was again turned to the right 90°;. Thus the door was opened by the dogs successively at the N.W.

This work also discusses the most well-know issues in the Twitter genre: the use of hashtags and the amount of OOVs, with especial focus on comparing the differences between formal

3.1 Sentiment Analysis module The sentiment analysis module relies in a SVM machine-learning model that is trained with data composed of features extracted from the TASS

We present and discuss an exper- imental setting to observe language through tweets, with a particular emphasis on the impact of visualization tech- niques on time series (messages

Green 11 , depends on comparing the dynamics of a (clas- sical) reduced distribution function for s particles with that of an isolated set of s particles, essentially expanding

Our lexical analysis approach using logistic regression produces the best results at 0.43 MCC, we describe a promising community labeling model, and discuss our attempts to

In bot detection, we used features related to behavior of the account, sentiment and va- riety of posts, in gender detection function words and emoticons.. These features were

Therefore, in this work we propose an innovative approach that combines deep learning frame- work with linguistic features specific for this issue.. This approach has been evaluated