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Positional embedding and context representation for Indonesian targeted aspect-based sentiment analysis
Ilmania A.a, Purwarianti A.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Inspired by some research on entity level and aspect level sentiment analysis, we build a two-step classification system to tackle the challenges of targeted aspect-based sentiment analysis (TABSA). The system consists of aspect categorization and sentiment classification. We employ a Bi-LSTM network, position features i.e. context representation and positional embedding on both classifiers, and one-hot aspect vectors in sentiment classifier to give additional information. We conducted experiments using 900 Indonesian reviews related to automotive. Despite not using complex feature because of limitation in low-resource language, the system can still make decent prediction. We found that the use of positional embedding and context representation in our model is proven to be able to capture the relation between entity and aspect-sentiment. The use of one-hot aspect vectors was also found to be slightly helpful for sentiment classifier to capture polarity related to a specific aspect. Experiment results showed that our approach outperformed the self-training baseline in terms of F1 and accuracy scores.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification system,Context representation,Entity-level,Low resource languages,Positional embedding,Self training,Sentiment classification[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bi-LSTM,Context representation,Positional embedding,Targeted aspect-based sentiment analysis[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]ACKNOWLEDGEMENT This work is a part of “Intelligent System to Monitor Gadget Usage in Teenagers using Machine Learning Technique” research and partially funded by the Ministry of Research and Higher Education of Indonesia. We also would like to give Prosa gratitude for helping to provide the dataset for this research.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICACSIS47736.2019.8979737[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]