Tao Liu
ABSTRACT
Automatic analysis of sentiments expressed in large scale online reviews is very important for intelligent business applications. Sentiment classification is the most popular task of sentiment analysis, which is more challenging than traditional topic-based text classification. Basic features, such as vocabulary words, are not enough to classify sentiments well. Deep Belief Network (DBN) is introduced to discover more abstract features of sentiments. To capture full information of the features, large-size network can be constructed, but at the same time, large-size network tends to over fit the training data and even noise, which will reduce the generalization ability of the network. In this paper, L2-norm Deep Belief Network (L2DBN) is proposed, which uses L2-norm regularization to optimize the network parameters of DBN. L2DBN is first initialized by an unsupervised layer-wise training algorithm, and then fine-tuned by a supervised procedure. Network parameters are optimized using both classification loss and network complexity. Experimental results show that the proposed L2DBN outperforms the state-of-the-art method and the basic DBN on golden, noisy and heterogeneous datasets.
- Turney P. D. 2002. Thumbs up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of ACL (USA, July). 417--424. Google Scholar
Digital Library
- Pang B., Lee L., and Vaithyanathan S. 2002. Thumbs Up? Sentiment Classification Using Machine Learning Techniques. In Proceedings of EMNLP. (USA, July). 79--86. Google ScholarDigital Library
- Pang B. and Lee L. 2008. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2, 1--2, 1--135. Google ScholarDigital Library
- Liu B. eds. 2010. Sentiment Analysis and Subjectivity: Handbook of Natural Language Processing (Second Edition). Boca Raton, FL: CRC Press, Taylor and Francis Group.Google Scholar
- Hinton G. E. and Salakhutdinov R. R. 2006. Reducing the Dimensionality of Data with Neural Networks. Science 313, 5786, 504--507.Google Scholar
- Mnih A. and Hinton G. E. 2008. A Scalable Hierarchical Distributed Language Model. In Proceedings of NIPS (Canada). 1081--1088.Google Scholar
- Hastie, T., Tibshirani, R., Friedman J. 2001. The Elements of Statistical Learning. Springer-Verlag.Google Scholar
- Blitzer J., Dredze M., and Pereira F. 2007. Biographies, Bollywood, Boom-Boxes and Blenders: Domain Adaptation for Sentiment Classification. In Proceedings of ACL (Czech Republic, June 23rd-30th). 440--447.Google Scholar
- Dasgupta S. and Ng V. 2009. Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification. In Proceedings of ACL-IJCNLP (Singapore, August 2--7). 701--709. Google ScholarDigital Library
Index Terms
- Sentiment classification via l2-norm deep belief network
Recommendations
Sentence-level Sentiment Classification with Weak Supervision
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalSentence-level sentiment classification is important to understand users' fine-grained opinions. Existing methods for sentence-level sentiment classification are mainly based on supervised learning. However, it is difficult to obtain sentiment labels of ...
Sentiment Lexicon Enhanced Neural Sentiment Classification
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge ManagementSentiment classification is an important task in the sentiment analysis field. Many deep learning based sentiment classification methods have been proposed in recent years. However, these methods usually rely on massive labeled texts to train sentiment ...
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge ManagementAspect-level sentiment classification is a fine-grained sentiment analysis task, which aims to predict the sentiment of a text in different aspects. One key point of this task is to allocate the appropriate sentiment words for the given aspect.Recent ...
Comments