In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

In a recent work on sentiment analysis in Turkish BIBREF10, they learn embeddings using Turkish social media. They use the word2vec algorithm, create several unsupervised hand-crafted features, generate document vectors and feed them as input into the support vector machines (SVM) approach. We outperform this baseline approach using more effective word embeddings and supervised hand-crafted features. 
 Question: What baseline method is used?
using word2vec to create features that are used as input to the SVM

Most lexical resources for sentiment analysis are in English. To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5  
 Question: How was the training data translated?
using the machine translation platform Apertium 

CNN can also be employed on the sarcasm datasets in order to identify sarcastic and non-sarcastic tweets. We term the features extracted from this network baseline features, the method as baseline method and the CNN architecture used in this baseline method as baseline CNN. Since the fully-connected layer has 100 neurons, we have 100 baseline features in our experiment.  
 Question: What are the network's baseline features?

 The features extracted from CNN.