Aspect-Based Sentiment Analysis Using The Pre-trained Language Model BERT

Hoang, Mickel; Bihorac, Alija

Aspect-Based Sentiment Analysis Using The Pre-trained Language Model BERT

dc.contributor.author	Hoang, Mickel
dc.contributor.author	Bihorac, Alija
dc.contributor.department	Chalmers tekniska högskola / Institutionen för data och informationsteknik	sv
dc.contributor.examiner	Johansson, Richard
dc.contributor.supervisor	Rouces Gonzalez, Jacobo
dc.date.accessioned	2019-07-19T09:43:06Z
dc.date.available	2019-07-19T09:43:06Z
dc.date.issued	2019	sv
dc.date.submitted	2019
dc.description.abstract	Sentiment analysis has become popular in both research and business due to the increasing amount of opinionated text generated by Internet users. Sentiment analysis focuses on classifying the overall sentiment of a text, which may not include important information such as different sentiment associated to specific aspects in the text. The more complex task of identifying the sentiment of certain aspects in a text is known as Aspect-Based sentiment analysis (ABSA). This paper show the potential of using the contextual word representations from pre-training language models to solve out-of-domain ABSA by constructing a generic ABSA model using BERT, together with the method of fine-tuning the model to make it learn when aspects are related or unrelated to a text. To our knowledge, no other existing work has been done on out-of-domain ABSA for aspect classification.	sv
dc.identifier.coursecode	DATX05	sv
dc.identifier.uri	https://hdl.handle.net/20.500.12380/300062
dc.language.iso	eng	sv
dc.setspec.uppsok	Technology
dc.subject	Pre-training	sv
dc.subject	science	sv
dc.subject	computer science	sv
dc.subject	engineering	sv
dc.subject	thesis	sv
dc.subject	natural language processing	sv
dc.subject	Deep learning	sv
dc.subject	contextual word representation	sv
dc.subject	Aspect-based sentiment analysis	sv
dc.subject	BERT	sv
dc.title	Aspect-Based Sentiment Analysis Using The Pre-trained Language Model BERT	sv
dc.type.degree	Examensarbete för masterexamen	sv
dc.type.uppsok	H

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