Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach

By Duyu Tang, Furu Wei, Bing Qin, M. Zhou, Ting-Wei Liu. 2014

191 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Sentiment & Opinion Analysis

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Citations in the corpus

EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks (2017, background): “A line of studies in the literature (e.g., (Labutov and Lip-son, 2013; Maas et al., 2011; Tang et al., 2014b,a) aim to learn sentiment-speciﬁc word embeddings (Bengio et al., 2003; Mikolov et al., 2013) from neighboring text.”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, methodology): “Tang et al. (2014a) created a sentiment analysis system that came first in the 2014 SAT subtask on a tweets dataset.”
User Modeling with Neural Network for Review Rating Prediction (2015, methodology): “Deep learning has been proven to be effective for many sentiment analysis tasks [Socher et al., 2013; Tang et al., 2014a; Xu et al., 2014].”
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora (2016, background): “Twitter: Numerous works attempt to induce Twitter-speciﬁc sentiment lexicons using supervised approaches and features unique to that domain (e.g., follower graphs; Speriosu et al., 2011).”
Sentiment Embeddings with Applications to Sentiment Analysis (2016, result): “It is a substantial extension of our previous works in [1], [2], [3].”
A review of natural language processing techniques for opinion mining systems (2017, background): “Learning lexicons from annotated corpora in a particular domain would obtain a better performance [123, 124, 125].”
Target-Dependent Sentiment Classification with Long Short Term Memory (2015, methodology): “Since the benchmark dataset from [Dong et al., 2014] comes from Twitter, we compare between sentiment-specific word embedding (SSWE) 2 [Tang et al., 2014b] and Glove vectors 3 [Pennington et al., 2014] .”
Target-Dependent Twitter Sentiment Classification with Rich Automatic Features (2015, methodology): “Neural pooling functions [Collobert et al., 2011; Tang et al., 2014b; Kalchbrenner et al., 2014] are used to extract features automatically from both contexts according to each type of embeddings.”
Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis (2015, background): “Fortunately, the ever developing phrase-level sentiment analysis techniques [18, 10, 36, 29] have made it possible to extract, summarize and structure the rich product features and user preferences automatically from free-text reviews, and have begun to be leveraged for personalized recommendation t”
Learning Semantic Representations of Users and Products for Document Level Sentiment Classification (2015, methodology): “, 2014) use local and global contexts, (Maas et al., 2011; Labutov and Lipson, 2013; Tang et al., 2014b; Tang et al., 2014a; Zhou et al., 2015) further incorporate sentiment of texts.”