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paper titleconferenceyearfirst authortype一句話描述experiment datasetcode
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Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style TransferNAACL2018Juncen Lisentiment transfer把句子中的 content 以及 style 分開，在保留 content 條件下轉換 styleYelp, Amazon, CaptionsO
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A C-LSTM Neural Network for Text Classificationarxiv2015Chunting ZhouSA
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ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATIONICLR2017Takeru MiyatoSA找出 adversarial example，對 input 加上小擾動不應該改變語意IMDB, Elec, Rotten Tomatoes, DBpedia, RCV1O
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Character-level Convolutional Networks for Text ClassificationNIPS2015Xiang ZhangSA
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Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short TextsCOLING2016Xingyou WangSA
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Convolutional Neural Networks for Sentence ClassificationEMNLP2014Yoon KimSA
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Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-levelarxiv2016Rie JohnsonSA
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Deep Convolutional Neural Networks for Sentiment Analysis of Short TextsCOLING2014C ́ıcero Nogueira dos SantosSA
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Document Modeling with Gated Recurrent Neural Network for Sentiment ClassificationEMNLP2015Duyu TangSA
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Effective Use of Word Order for Text Categorization with Convolutional Neural NetworksNAACL2015Rie JohnsonSA
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Efficient Character-level Document Classification by Combining Convolution and Recurrent Layersarxiv2016Yijun XiaoSA用 CNN 抽取 char 資訊，再用 RNN 獲取長期資訊AG, sogou, DBPedia, Yelp, Yahoo, Amazon
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Hierarchical Attention Networks for Document ClassificationNAACL2016Zichao YangSA
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Learning Sentiment-Specific Word Embedding for Twitter Sentiment ClassificationACL2014Duyu TangSA
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Learning to Generate Reviews and Discovering Sentimentarxiv2017Alec RadfordSA
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Molding CNNs for text: non-linear, non-consecutive convolutionsEMNLP2015Tao LeiSA
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Semi-supervised Convolutional Neural Networks for Text Categorization via Region EmbeddingNIPS2015Rie JohnsonSA
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Semi-supervised Sequence LearningNIPS2015Andrew M. DaiSA
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Sentiment Classification using Images and Label Embeddingsarxiv2017Laura GraesserSA
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Using Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment ClassificationACL2005Jonathon ReadSA
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Linguistically Regularized LSTM for Sentiment ClassificationACL2017Qiao QianSA在 neural network 中加入語言學規則，很有趣的 paperMR, SST
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Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text CategorizationACL2017Ye ZhangSA利用 weight sharing，將 prior knowledge 引入，幫助分類MR, CR, MPQA, AN, CL, ST, PB
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Overcoming Language Variation in Sentiment Analysis with Social AttentionTACL2017Yi YangSA在社群網路中，有關係的人，會用相似的方式去使用語言SemEval2013
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Rationale-Augmented Convolutional Neural Networks for Text ClassificationEMNLP2016Ye ZhangSA利用 sentence-level label 來輔助 document-level 情感分類MR
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Deep Pyramid Convolutional Neural Networks for Text CategorizationACL2017Rie JohnsonSA利用 deep word-level CNN 來做文件分類，達到 state-of-the-artAG, sogou, DBPedia, Yelp, Yahoo, Amazon
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Neural Sentiment Classification with User and Product AttentionEMNLP2016Huimin Chenuser / product
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Learning Word Vectors for Sentiment AnalysisACL2011Andrew L. Maassent / doc modeling
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A Convolutional Neural Network for Modelling SentencesACL2014Nal Kalchbrennersent / doc modeling
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A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SEN- TENCE EMBEDDINGSICLR2017Sanjeev Arorasent / doc modeling
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Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
IEEE/ACM Transactions on Audio, Speech, and Language Processing
2015Hamid Palangisent / doc modeling
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Distributed Representations of Sentences and DocumentsICML2014Quoc Lesent / doc modeling
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Learning Generic Sentence Representations Using Convolutional Neural NetworksEMNLP2017Zhe Gansent / doc modeling
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A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classificationarxiv2016Ye Zhangexp
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Twitter Sentiment Classification using Distant Supervision-2009Alec Gotwitter
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Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment ClassificationWWW2017Jan Deriumulti-lingual
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Multilingual Hierarchical Attention Networks for Document Classificationarxiv2017Nikolaos Pappasmulti-lingual
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Sentiment Analysis with Deeply Learned Distributed Representations of Variable Length Texts-2015James Hongcs224d
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*Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment ClassificationACL2014Li Dongaspect / targetACL2014-short (Dong et al. 2014)X
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SemEval-2014 Task 4: Aspect Based Sentiment AnalysisSemEval2014Maria Pontikiaspect / targetSemEval 2014 documentSemEval2014
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Target-Dependent Twitter Sentiment Classification with Rich Automatic FeaturesIJCAI2015Duy-Tin Voaspect / target對 context 進行多種 pooling，並利用 sentiment lexicon 來濾掉不重要的字ACL2014-short (Dong et al. 2014)X
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Neural Networks for Open Domain Targeted SentimentEMNLP2015Meishan Zhangaspect / target將 NN 結合到 CRF-based 的方法中Mitchell et al. 2013O
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Attention-based LSTM for Aspect-level Sentiment ClassificationEMNLP2016Yequan Wangaspect / target為了更好的描述 aspect word，用了 aspect embeddingSemEval2014X
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Aspect Level Sentiment Classification with Deep Memory NetworkEMNLP2016Duyu Tangaspect / target首篇將 memory network 用在 sentiment networkSemEval2014X
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A Hierarchical Model of Reviews for Aspect-based Sentiment AnalysisEMNLP2016Sebastian Ruderaspect / target用 hierarchical 架構考慮句子間的關係SemEval2016X
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Effective LSTMs for Target-Dependent Sentiment ClassificationCOLING2016Duyu Tangaspect / target把 input 丟進 LSTM 時，會加上 target embeddingACL2014-short (Dong et al. 2014)X
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Gated Neural Networks for Targeted Sentiment AnalysisAAAI2016Meishan Zhangaspect / target利用 gate 來控制 LSTM 產物之間結合的比重ACL2014-short (Dong et al. 2014), MPQA + Mitchell et al. 2013O
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Attention Modeling for Targeted SentimentEACL2017Jiangming Liuaspect / target使用 bi-LSTM + attention 來 model context，並使用 gate 將 LSTM 產物組合ACL2014-short (Dong et al. 2014), MPQA + Mitchell et al. 2013O
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Attention-Based LSTM for Target-Dependent Sentiment ClassificationAAAI2017Min Yangaspect / target兩種 attention 方式，paper 質量不高ACL2014-short (Dong et al. 2014)X
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Document-Level Multi-Aspect Sentiment Classification as Machine ComprehensionEMNLP2017Yichun Yinaspect / target把 sentiment analysis 當成 reading comprehension 問題來解，可以套用現有的架構TripAdvisor, BeerAdvocateO
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Recurrent Attention Network on Memory for Aspect Sentiment AnalysisEMNLP2017Peng Chenaspect / target對 LSTM hidden state 做複數次 attention，每次 attention 間用 GRU 連接SemEval2014, ACL2014-short (Dong et al. 2014), 自收集中文 datasetX
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Interactive Attention Networks for Aspect-Level Sentiment ClassificationIJCAI2017Dehong Maaspect / targettarget 跟 context 做 attention 時都會考慮對方SemEval2014X
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Aspect-Based Sentiment Analysis Based on Multi-Attention CNN
Journal of Computer Research and Development
2017Liang Binaspect / target將 attention mechanism 與 CNN 結合SemEval2014, automotive-domain data (ADD)X
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Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTMAAAI2018Yukun Maaspect / target利用 commonsense knowledge 輔助純文字情感分析SemEval2015, SentiHoodX
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Transformation Networks for Target-Oriented Sentiment ClassificationACL2018Xin Liaspect / target考慮 target & context 的交互作用，提出不同於常規的 attentionSemEval2014, ACL2014-short (Dong et al. 2014)O
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Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short TextACL2016Duy Tin Volexiconpredict-based 取代 count-based 來找 sentiment lexiconSemEval13
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Context-Sensitive Lexicon Features for Neural Sentiment AnalysisEMNLP2016Zhiyang Tenglexicon情感字典中的分數，要隨著不同 context 而改變SemEval13, SSTO
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Refining Word Embeddings for Sentiment AnalysisEMNLP2017Liang-Chih Yulexicon / word embed利用情感字典的分數來更新 word embeddingSSTX
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Sentiment Analysis on Tweets about Diabetes - An Aspect-Level Approach
Computational and Mathematical Methods in Medicine
2017María del Pilar Salas-Záratelexicon運用特定領域的辭典來找出 aspect words
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Lexicon Integrated CNN Models with Attention for Sentiment AnalysisWASSA2017Bonggun Shinlexicon利用 sentiment lexicon 增加 CNN model 效能SemEval16, SST
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AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysisAAAI2015Erik Cambriaknowledge-based把 common knowledge matrix 用 randon projection 降維，輔助情感分析
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Rationalizing Neural PredictionsEMNLP2016Tao Leirationale extraction自動抽取 rationale，能擴展到多種任務上，超越 attention-based methodsBeerAdvocate, AskUbuntu
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