Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings

By Pengfei Liu, Shafiq R. Joty, Helen Mei-Ling Meng. Conference on Empirical Methods in Natural Language Processing, 2015

446 citations recorded by the source corpus.

The tasks in fine-grained opinion mining can be regarded as either a token-level sequence labeling problem or as a semantic compositional task. We propose a general class of discriminative models based on recurrent neural networks (RNNs) and word embeddings that can be successfully applied to such tasks without any taskspecific feature engineering effort. Our experimental results on the task of opinion target identification show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based models. Our framework is flexible, allows us to incorporate other linguistic features, and achieves results that rival the top performing systems in SemEval-2014.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8959a480a77c29c223533e1438268d02bb8ecf7b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. B. Liu
2. Hu Xu
3. Lei Shu
4. Bing Liu
5. Philip S. Yu
6. Shuai Wang
7. Sebastian Ruder
8. Lidong Bing

Citations in the corpus

• Multitask learning for semantic sequence prediction under varying data conditions (2016, methodology): “Bi-LSTM have recently successfully been used for a variety of tasks (Collobert et al., 2011; Huang et al., 2015; Dyer et al., 2015; Ballesteros et al., 2015; Kiperwasser and Goldberg, 2016; Liu et al., 2015; Plank et al., 2016).”
• Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model (2016, methodology): “…(NN) such as convolutional neural networks (CNN) (Kim, 2014; Kalchbrenner et al., 2014), recurrent neural networks (RNN) (Graves, 2012; Irsoy and Cardie, 2014) and long short-term memory (LSTM) (Wang et al., 2015; Liu et al., 2015) have been successfully employed for categorical sentiment analysis.”
• Aspect Sentiment Quad Prediction as Paraphrase Generation (2021, background): “Early studies focus on the prediction of a single element such as aspect term extraction (Liu et al., 2015; Xu et al., 2018), as-”
• Deep learning for sentiment analysis: A survey (2018, methodology): “55,62,87,89,95”
• A review of natural language processing techniques for opinion mining systems (2017, methodology): “[139] proposed a general class of discriminative models based on RNNs and word embeddings that can be successfully applied to fine-grained opinion mining without any task-specific feature engineering effort.”
• Multi-Task Learning of Keyphrase Boundary Classification (2017, background): “Several variants have been introduced, including hard sharing of selected layers (Søgaard and Goldberg, 2016) and sharing of parts (subspaces) of layers (Liu et al., 2015).”
• Target-Dependent Sentiment Classification with Long Short Term Memory (2015, background): “…sentiment classification [Socher et al., 2013b; Tang et al., 2015], opinion expression extraction [Irsoy and Cardie, 2014b], building sentiment lexicon [Tang et al., 2014a], open domain sentiment analysis [Zhang et al., 2015], fine-grained opinion mining [Liu et al., 2015], etc.”
• Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis (2016, methodology): “To the best of our knowledge, the work of Liu et al. (2015) and Yin et al. (2016) are the most related to ours.”
• Distributed Representations of Tuples for Entity Resolution (2017, background): “, convolutional network for n-gram level composition [14], recurrent network for sequential composition [34, 35], and recursive network for hierarchical tree-based composition [48].”
• How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers (2017, background): “Recursive neural networks (RNN), in particular its variant Long Short Memory Networks (LSTM) [12], have been very successful in modeling sequential data [43, 23], with wide applications in text classification [16], especially in sentiment analysis [14, 19].”
• Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss (2016, methodology): “…al., 2015), POS tagging (Ling et al., 2015; Wang et al., 2015), transition-based dependency parsing (Ballesteros et al., 2015; Kiperwasser and Goldberg, 2016), fine-grained sentiment analysis (Liu et al., 2015), syntactic chunking (Huang et al., 2015), and semantic role labeling (Zhou and Xu, 2015).”
• Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training (2019, unknown): “Rule-based or traditional learning models for aspect detection have been outperformed by supervised neural networks (Liu et al., 2015; Poria et al., 2016; Zhang et al., 2018).”
• Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review (2019, background): “An interesting work by Tang, Qin, Feng, et al. (2015) proposed adding attention layers in their bidirectional LSTM.”
• Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018, methodology): “MIN (Li and Lam, 2017) is a multi-task learning framework that has (1) two LSTMs for jointly extraction of aspects and opinions, and (2) a third LSTM for discriminating sentimental and nonsentimental sentences.”