Aspect-Based Sentiment Analysis Using The Pre-trained Language Model BERT
Typ
Examensarbete för masterexamen
Program
Publicerad
2019
Författare
Hoang, Mickel
Bihorac, Alija
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Pre-training , science , computer science , engineering , thesis , natural language processing , Deep learning , contextual word representation , Aspect-based sentiment analysis , BERT