  Bidirectional Long Short-Term Memory Network (Bi-LSTM) has shown promising
performance in sentiment classification task. It processes inputs as sequence
of information. Due to this behavior, sentiment predictions by Bi-LSTM were
influenced by words sequence and the first or last phrases of the texts tend to
have stronger features than other phrases. Meanwhile, in the problem scope of
Indonesian sentiment analysis, phrases that express the sentiment of a document
might not appear in the first or last part of the document that can lead to
incorrect sentiment classification. To this end, we propose the using of an
existing document representation method called paragraph vector as additional
input features for Bi-LSTM. This vector provides information context of the
document for each sequence processing. The paragraph vector is simply
concatenated to each word vector of the document. This representation also
helps to differentiate ambiguous Indonesian words. Bi-LSTM and paragraph vector
were previously used as separate methods. Combining the two methods has shown a
significant performance improvement of Indonesian sentiment analysis model.
Several case studies on testing data showed that the proposed method can handle
the sentiment phrases position problem encountered by Bi-LSTM.
