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.

[EX Q]: The primary feed for the analysis collected INLINEFORM0 million tweets containing the keywords `breast' AND `cancer'.  
 Question: How were breast cancer related posts compiled from the Twitter streaming API?
[EX A]: By using  keywords `breast' AND `cancer' in tweet collecting process. 


[EX Q]: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . 
 Question: How do they represent input features of their model to train embeddings?
[EX A]: a vector of frame-level acoustic features

[EX Q]: We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. 
 Question: What are the datasets used for training?
[EX A]:
SemEval 2016 Task 6 BIBREF7 Stance Sentiment Emotion Corpus (SSEC) BIBREF15