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.

Q:  In particular, we use Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset for the training Sarcasm Datasets Used in the Experiment
This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.

Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.

We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics. 
 Question: Which benchmark datasets are used?

A: Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset   dataset was created by BIBREF8  English dataset from BIBREF8  dataset from The Sarcasm Detector
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Q: The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .  
 Question: Where are the hotel reviews from?

A: TripAdvisor
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Q: Our data are collected from the yelp academic dataset, provided by Yelp.com, a popular restaurant review website. The data set contains three types of objects: business, user, and review, where business objects contain basic information about local businesses (i.e. restaurants), review objects contain review texts and star rating, and user objects contain aggregate information about a single user across all of Yelp. Table TABREF31 illustrates the general statistics of the dataset. 
 Question: Does they focus on any specific product/service domain?

A:
local businesses (i.e. restaurants)
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