In this paper, we present our contribution in SemEval2014 ABSA task, some supervised methods for Aspect-Based Sentiment Analysis of restaurant and laptop reviews are proposed, implemented and evaluated. We focus on determining the aspect terms existing in each sentence, finding out their polarities, detecting the categories of the sentence and the polarity of each category. The evaluation results of our proposed methods exhibit a significant improvement in terms of accuracy and f-measure over all four subtasks regarding to the baseline proposed by SemEval organisers.
A supervised learning approach for determining the aspect terms in a sentence and their polarity is applied REF on SemEval2014 ABSE task.
Supervised Methods for Aspect-Based Sentiment Analysis
SemEval@COLING
Computer Science