Each of 6, 248 sentences are annotated with a target element and the task is to predict the sentiment regarding that element (positive, negative or neutral).
for our final task we consider a much more sophisticated model using LSTMs (with Python's KERAS 3 ) to perform target-dependent sentiment classification (Tang et al., 2016) using twitter-specific sentiment word vectors  on the benchmark twitter dataset collected by Dong et al (2014).