Target-Dependent Sentiment Analysis of Tweets Using Bidirectional Gated Recurrent Neural Networks

The task of target-dependent sentiment analysis aims to identify the sentiment polarity towards a certain target in a given text. All the existing models of this task assume that the target is known. This fact has motivated us to develop an end-to-end target-dependent sentiment analysis system. To the extent of our knowledge, this is the first system that identifies and extract the target of the tweets. The proposed system is composed of two main steps. First, the targets of the tweet to be analysed are extracted. Afterwards, the system identifies the polarities of the tweet towards each extracted target. We have evaluated the effectiveness of the proposed model on a benchmark dataset from Twitter. The experiments show that our proposed system outperforms the state-of-the-are methods for target-dependent sentiment analysis.

[1]  Ronen Feldman,et al.  Techniques and applications for sentiment analysis , 2013, CACM.

[2]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[3]  Xiaolong Wang,et al.  Modeling Mention, Context and Entity with Neural Networks for Entity Disambiguation , 2015, IJCAI.

[4]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[5]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[6]  Ioannis Hatzilygeroudis,et al.  Recognizing emotions in text using ensemble of classifiers , 2016, Eng. Appl. Artif. Intell..

[7]  Yue Zhang,et al.  Gated Neural Networks for Targeted Sentiment Analysis , 2016, AAAI.

[8]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[10]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[11]  Yue Zhang,et al.  Target-Dependent Twitter Sentiment Classification with Rich Automatic Features , 2015, IJCAI.

[12]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[13]  Jian Su,et al.  NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features , 2016, *SEMEVAL.

[14]  Tiejun Zhao,et al.  Target-dependent Twitter Sentiment Classification , 2011, ACL.

[15]  Bing Liu,et al.  Opinion Mining and Sentiment Analysis , 2011 .

[16]  Dan Roth,et al.  Design Challenges and Misconceptions in Named Entity Recognition , 2009, CoNLL.

[17]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[18]  Qian Liu,et al.  Automated rule selection for opinion target extraction , 2016, Knowl. Based Syst..

[19]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[20]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[21]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Ming Zhou,et al.  Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification , 2014, ACL.

[24]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[25]  Antonio Moreno,et al.  Do Local Residents and Visitors Express the Same Sentiments on Destinations Through Social Media? , 2017, ENTER.

[26]  Philipp Cimiano,et al.  Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture , 2016, SemWebEval@ESWC.

[27]  Nicolas Nicolov,et al.  Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations , 2009, ICWSM.

[28]  Xiaocheng Feng,et al.  Target-Dependent Sentiment Classification with Long Short Term Memory , 2015, ArXiv.

[29]  Wang Ling,et al.  Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation , 2015, EMNLP.

[30]  Qian Liu,et al.  Automated Rule Selection for Aspect Extraction in Opinion Mining , 2015, IJCAI.

[31]  Antonio Moreno,et al.  SentiRich: Sentiment Analysis of Tweets Based on a Rich Set of Features , 2016, CCIA.

[32]  Saif Mohammad,et al.  NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.

[33]  Erik F. Tjong Kim Sang,et al.  Representing Text Chunks , 1999, EACL.

[34]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.