We improved sentiment classifier for predicting document-level sentiments from Twitter by using multi-channel lexicon embedidngs. The core of the architecture is based on CNNBiLSTM that can capture high level features and long term dependency in documents. We also applied multi-channel method on lexicon to improve lexicon features. The macroaveraged F1 score of our model outperformed other classifiers in this paper by 1-4%. Our model achieved F1 score of 64% in SemEval Task 4 (2013-2016) datasets when multichannel lexicon embedding was applied with 100 dimensions of word embedding.
At the multichannel-level, Yoon and Kim REF improved the sentiment classifier for predicting document-level sentiments from Twitter by using multichannel lexicon embeddings and applying the multichannel method on lexicon to improve lexicon features.
Multi-Channel Lexicon Integrated CNN-BiLSTM Models for Sentiment Analysis.
ROCLING
Computer Science