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Aspect Based Sentiment Classification of Multilingual Customer Reviews

Gupta, Y. (2021) Aspect Based Sentiment Classification of Multilingual Customer Reviews.

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Abstract:This work aims to find suitable techniques to improve the performance of a state of the art system mentioned in 'Utilising BERT for Aspect Based Sentiment Analysis using auxiliary sentences' for the task of aspect based sentiment analysis of customer reviews for a multi-lingual use case. Authors state that the mentioned system works better because of augmented data for training and it's better sense for sense of sentence pair classification. Three motivated changes are experimented with the state of the art design and training techniques to verify if the reasons stated by authors are actually the reasons behind the increase in performance and also improve the performance of the state of the art system. The baseline systems are developed as demonstrated by authors of but unlike the authors, two baseline systems are developed one with a pre-trained BERT model and one with a pre-trained BERT-multilingual model. After experimentation, it is concluded that the state of the art can indeed be redesigned to train with multi-task learning (without auxiliary sentences) to provide better results. It is also concluded that the reason behind the increased performance in the state of the art system is multi-task learning which takes place in effect when trained with auxiliary sentences and a better sense of sentence pair classification for the model and not the increased size of training set. Instead, it is observed that the increased data hinders the learning potential of the systems. The dataset for experimentation is provided by Daimler A.G. subsidy, MercedesBenz Customer Assistance Center Maastricht N.V. which contains multilingual customer reviews labelled for different aspects of their business.
Item Type:Essay (Master)
Mercedes - Benz Customer Assistance Center, Maastricht, Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:50 technical science in general, 54 computer science, 85 business administration, organizational science
Programme:Computer Science MSc (60300)
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