NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis

TL;DR

This paper presents NileTMRG's systems for Arabic sentiment analysis in SemEval-2017, utilizing ensemble classifiers and a sentiment lexicon, achieving top rankings across three subtasks.

Contribution

The authors developed an ensemble approach combining CNN, MLP, and logistic regression for Arabic sentiment analysis, achieving state-of-the-art results.

Findings

NileTMRG ranked first in all three Arabic subtasks.

Ensemble classifiers improved accuracy over individual models.

Using a scored lexicon enhanced sentiment classification performance.

Abstract

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.