TY - JOUR
T1 - Multi-source social media data sentiment analysis using bidirectional recurrent convolutional neural networks
AU - Abid, Fazeel
AU - Li, Chen
AU - Alam, Muhammad
N1 - Publisher Copyright:
© 2020
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Subjectivity detection in the text is essential for sentiment analysis, which requires many techniques to perceive unanticipated means of communication. Few accomplishments adapted to capture the syntactic, semantic, and contextual sentimental information via distributed word representations (DWRs)1. This paper, concatenating the DWRs through a weighted mechanism on Recurrent Neural Network (RNN) variants joint with Convolutional Neural network (CNN) distinctively involving weighted attentive pooling (WAP)2. Whereas, CNNs with traditional pooling operations comprise many layers merely able to capture enough features. Our considerations empower the sentiment analysis over DWRs contains Word2vec, FastText, and GloVe to produce dense efficient concatenated representation (DECR)3 to hold long term dependencies on a single RNN layer acquired by Parts of Speech Tagging (POS) explicitly with verbs, adverbs, and noun only. Then use these representations gained in a way, inputted to CNN contain single convolution layer engaging WAP on multi-source social media data to handle the issues of syntactic and semantic regularities as well as out of vocabulary (OOV) words. Experimentations demonstrate that DWRs together with proposed concatenation qualified in resolving the mentioned issues by moderate hyper-parameter configurations. Our architecture devoid of stacking multiple layers achieved modest accuracy of 89.67% by DECR-Bi-GRU-CNN (WAP) on IMDB as compared to random initialization 81.11% on SST.
AB - Subjectivity detection in the text is essential for sentiment analysis, which requires many techniques to perceive unanticipated means of communication. Few accomplishments adapted to capture the syntactic, semantic, and contextual sentimental information via distributed word representations (DWRs)1. This paper, concatenating the DWRs through a weighted mechanism on Recurrent Neural Network (RNN) variants joint with Convolutional Neural network (CNN) distinctively involving weighted attentive pooling (WAP)2. Whereas, CNNs with traditional pooling operations comprise many layers merely able to capture enough features. Our considerations empower the sentiment analysis over DWRs contains Word2vec, FastText, and GloVe to produce dense efficient concatenated representation (DECR)3 to hold long term dependencies on a single RNN layer acquired by Parts of Speech Tagging (POS) explicitly with verbs, adverbs, and noun only. Then use these representations gained in a way, inputted to CNN contain single convolution layer engaging WAP on multi-source social media data to handle the issues of syntactic and semantic regularities as well as out of vocabulary (OOV) words. Experimentations demonstrate that DWRs together with proposed concatenation qualified in resolving the mentioned issues by moderate hyper-parameter configurations. Our architecture devoid of stacking multiple layers achieved modest accuracy of 89.67% by DECR-Bi-GRU-CNN (WAP) on IMDB as compared to random initialization 81.11% on SST.
KW - Dense efficient concatenated representation “DECR”
KW - Distributed word representations
KW - FastText
KW - GloVe
KW - Recurrent and convolutional neural network
KW - Sentiment analysis
KW - Word2Vec
UR - http://www.scopus.com/inward/record.url?scp=85083313876&partnerID=8YFLogxK
U2 - 10.1016/j.comcom.2020.04.002
DO - 10.1016/j.comcom.2020.04.002
M3 - Article
AN - SCOPUS:85083313876
SN - 0140-3664
VL - 157
SP - 102
EP - 115
JO - Computer Communications
JF - Computer Communications
ER -