STRUCTURED SENTIMENT ANALYSIS MODELING WITH BART BASED ENCODER-DECODER AND BI-LSTM.
Sentiment Analysis or SA is one of the most widely used fields in Natural Language Processing or NLP today. SA itself also has a lot of branches such as aspect-based, end2end, targeted, and more. Because of this many branches, it becomes difficult to track the development of SA as whole. To overc...
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| 格式: | Final Project |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/77876 |
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| 總結: | Sentiment Analysis or SA is one of the most widely used fields in Natural Language Processing
or NLP today. SA itself also has a lot of branches such as aspect-based, end2end, targeted, and
more. Because of this many branches, it becomes difficult to track the development of SA as
whole. To overcome this, a solution is given in the form of Structured Sentiment Analysis which
aims to predict the holder, target, expression, and polarity of sentences collectively in a tuple.
One of the methods used is the BART-based Encoder-Decoder, but this method has a weakness
in handling long sentences and sentences without opinions. Therefore, a solution is proposed to
overcome these weaknesses by combining BART with an RNN or Recurrent Neural Network.
From a number of alternative RNNs that have been studied, a bi-LSTM was finally selected for
use. From the results obtained, the use of bi-LSTM can improve the performance of BART-based
Encoder-Decoder. |
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