  This paper describes two systems that were used by the authors for addressing
Arabic Sentiment Analysis as part of SemEval-2017, task 4. The authors
participated in three Arabic related subtasks which are: Subtask A (Message
Polarity Classification), Sub-task B (Topic-Based Message Polarity
classification) and Subtask D (Tweet quantification) using the team name of
NileTMRG. For subtask A, we made use of our previously developed sentiment
analyzer which we augmented with a scored lexicon. For subtasks B and D, we
used an ensemble of three different classifiers. The first classifier was a
convolutional neural network for which we trained (word2vec) word embeddings.
The second classifier consisted of a MultiLayer Perceptron, while the third
classifier was a Logistic regression model that takes the same input as the
second classifier. Voting between the three classifiers was used to determine
the final outcome. The output from task B, was quantified to produce the
results for task D. In all three Arabic related tasks in which NileTMRG
participated, the team ranked at number one.
