Aspect extraction for opinion mining with a deep convolutional neural network

By Soujanya Poria, Erik Cambria, Alexander Gelbukh. 2016

862 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “This fact was also observed in (Poria et al., 2016b), where authors performed sarcasm detection in Twitter1 texts using a CNN network.”
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (2016, methodology): “To this end, we leverage on VerbNet (Schuler, 2005), the largest English verb lexicon currently available, and Sentic LDA (Poria et al., 2016c), a classiﬁcation framework that integrates commonsense in the calculation of word distributions in the linear discriminant analysis (LDA) algorithm.”
Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis (2016, methodology): “Recent work on text modality has used CNN for sentimentrelated tasks such as sarcasm detection [13] and aspect-based opinion mining [14].”
SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis (2020, background): “More recent approaches have been focusing on the development of sentiment-specific word embeddings [44], which are able to encode more affective clues than regular word vectors, and on the use of context-aware subsymbolic approaches such as attention modeling [32, 33] and capsule networks [13, 66].”
Sentiment analysis using deep learning architectures: a review (2019, methodology): “Formore information onmultimodal sentiment analysis, the following popularworks can be referred (Soleymani et al. 2017; Chen et al. 2018; Poria et al. 2016b; Shah et al. 2016; Zhang et al. 2018a; Agarwal et al. 2019).”
Ensemble application of convolutional and recurrent neural networks for multi-label text categorization (2017, background): “Recently, several deep learning works have applied neural networks, such as CNN and RNN, to various NLP tasks, including multi-task learning [7], sentence classification [8], multimodal analysis [22], semantic relevance detection [10], [23], polarity detection [24], machine translation [25], sarcasm”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “In contrast, the sentiment analysis mostly concentrates on textual evaluations and opinion mining [21] on social events, marketing campaigns, and product preferences.”
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (2018, background): “Hence, aspect-based sentiment analysis (ABSA) (Pontiki et al. 2014; 2016; Poria, Cambria, and Gelbukh 2016) extends the typical setting of sentiment analysis with a more realistic assumption that polarity is associated with specific aspects (or product features) rather than the whole text unit.”
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training (2019, positive): “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).”
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018, methodology): “…applied to learn better features for supervised aspect extraction, e.g., us-ing LSTM (Williams and Zipser, 1989; Hochre-iter and Schmidhuber, 1997; Liu et al., 2015) and attention mechanism (Wang et al., 2017; He et al., 2017) together with manual features (Poria et al., 2016; Wang et al., 2016).”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “[45] em ployed a multi-level deep CNN to tag each word in a sentence as a possible aspect or nonaspect.”
A survey of neural network techniques in market trend analysis (2017, background): “Convolutional neural networks are a specialization of ANNs in that they simulate the interconnection of neurons in the visual cortex[12].”