Target-dependent Twitter Sentiment Classification
By Long Jiang, Mo Yu, M. Zhou, Xiaohua Liu, Tuo Zhao. Annual Meeting of the Association for Computational Linguistics, 2011
1,112 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/7ba9dcbd2e9163f2684b098786bbc3abb4784905
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Citations in the corpus
- Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “There has also been a considerable amount of work in subjectivity and sentiment analysis over the past decade, including, more recently, in microblogs (Barbosa and Feng, 2010; Bermingham and Smeaton, 2010; Pak and Paroubek, 2010; Bifet and Frank, 2010; Davidov et al., 2010; Li et al., 2010; Kouloump”
- Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (2019, background): “Some early work manually designs features, eg. sentiment lexicons and linguistic features, to train classifiers for aspect level sentiment classification (Jiang et al., 2011; Wagner et al., 2014).”
- Target-Dependent Sentiment Classification with Long Short Term Memory (2015, methodology): “In SVM-dep, target-dependent features [Jiang et al., 2011] are also concatenated as the feature representation.”
- Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks (2018, background): “In one recent work, Jiang et al. manually evaluated a Twitter sentiment classifier and showed that 40% of sentiment classification errors are because of not considering targets [6].”
- Attentional Encoder Network for Targeted Sentiment Classification (2019, methodology): “, 2008) and statistic-based methods (Jiang et al., 2011), mainly focus on extracting a set of features like sentiment lexicons features and bag-of-words features to train a sentiment classifier (Rao and Ravichandran, 2009).”
- Multi-grained Attention Network for Aspect-Level Sentiment Classification (2018, methodology): “Traditional approaches (Jiang et al., 2011; Kiritchenko et al., 2014; Vo and Zhang, 2015) regard this task as the text classification problem and design effective features, which are utilized in statistical learning algorithms for training a classifier.”
- A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
- Effective LSTMs for Target-Dependent Sentiment Classification (2015, methodology): “Therefore, standard text classification approach such as feature-based Supported Vector Machine (Pang et al., 2002; Jiang et al., 2011) can be naturally employed to build a sentiment classi-fier.”
- Target-Dependent Twitter Sentiment Classification with Rich Automatic Features (2015, methodology): “Evaluation metrics: We follow previous work [Jiang et al., 2011; Dong et al., 2014; Tang et al., 2014b] and use the Accuracy of three-way classification for each target as the main evaluation metric.”
- Transfer Capsule Network for Aspect Level Sentiment Classification (2019, methodology): “Traditional methods mostly use machine learning models with handcrafted features to build sentiment classifiers for ASC tasks (Jiang et al., 2011; Mohammad et al., 2013).”
- Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, methodology): “…as word n-grams and part-ofspeech n-grams have also been applied in tweet feature representations (Bermingham & Smeaton, 2010; Davidov, Tsur, & Rappoport, 2010; Go et al., 2009; Huang, Peng, Li, & Lee, 2013; Jiang et al., 2011; Kouloumpis et al., 2011; Mejova et al., 2013; Pak & Paroubek, 2010).”
- Lifelong Learning Memory Networks for Aspect Sentiment Classification (2018, methodology): “To involve aspect information, earlier studies relied on carefully engineered features [5]–[7], which require pattern designs, feature templates, or external resources.”
- Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms (2015, background): “Several other pieces of work focus on the extraction of sentiment at the post/check-in level [6], [16], [25].”
- Gated Neural Networks for Targeted Sentiment Analysis (2016, background): “There has been other previous work on targeted sentiment analysis (Jiang et al. 2011; Tang et al. 2014; Dong et al. 2014), which we did not include in Table 4.”