Target-Dependent Sentiment Classification with Long Short Term Memory
By Duyu Tang, Bing Qin, Xiaocheng Feng, Ting-Wei Liu. 2015
123 citations recorded by the source corpus.
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Citations in the corpus
- Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling (2016, background): “Tang et al. (2015) developed target dependent Long ShortTerm Memory Networks (LSTM (Hochreiter and Schmidhuber, 1997)), where target information is automatically taken into account.”
- Description Based Text Classification with Reinforcement Learning (2020, methodology): “It has broad applications such as sentiment analysis (Pang et al., 2002; Maas et al., 2011; Socher et al., 2013; Tang et al., 2014, 2015b), aspect sentiment clas-sification ( Jo and Oh, 2011; Tang et al., 2015a; Wang et al., 2015; Nguyen and Shirai, 2015; Tang et al., 2016b; Pontiki et al., 2016;…”
- Aspect Level Sentiment Classification with Deep Memory Network (2016, methodology): “(3) We compare with three LSTM models (Tang et al., 2015a)).”
- End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification (2017, background): “Recently, deep learning models [Socher et al., 2013; Tang et al., 2015] are exploited ∗†These two authors contributed equally to this work. in sentiment classification to automatically learn a good representation.”
- DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis (2017, result): “Our model is comparable to the work of (Wang et al., 2016; Tang et al., 2015).”
- Recurrent Attention Network on Memory for Aspect Sentiment Analysis (2017, methodology): “Because the datasets have unbalanced classes as shown in Table 1, Macro-averaged F-measure is also reported, as did in (Dong et al., 2014; Tang et al., 2015).”