{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/opinion-mining/papers/4","list_of":"/task/opinion-mining","task":"Opinion Mining","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":7,"rows_per_page":100,"rows":[301,400],"of":630,"counts":{"archive_papers_tagged":630,"with_a_code_link":64,"where_syntology_ran_a_sample":1,"not_listed_spam_title":0,"listed":630,"listed_where_code_ran":1,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":1,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/opinion-mining","prev":"/task/opinion-mining/papers/3","next":"/task/opinion-mining/papers/5","papers":[{"url":null,"slug":"structured-aspect-extraction","title":"Structured Aspect Extraction","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-greedy-inference-for-graph-based","title":"Effective Greedy Inference for Graph-based Non-Projective Dependency Parsing","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-aspect-specific-opinion","title":"Extracting Aspect Specific Opinion Expressions","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-abbreviations-for-chinese-named","title":"Generating Abbreviations for Chinese Named Entities Using Recurrent Neural Network with Dynamic Dictionary","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-do-i-look-publicity-mining-from","title":"How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-represent-review-with-tensor","title":"Learning to Represent Review with Tensor Decomposition for Spam Detection","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lifelong-rl-lifelong-relaxation-labeling-for","title":"Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"regularizing-text-categorization-with","title":"Regularizing Text Categorization with Clusters of Words","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/sentihood-targeted-aspect-based-sentiment","slug":"sentihood-targeted-aspect-based-sentiment","title":"SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods","date":"2016-10-12","arxiv_id":"1610.03771","repositories_listed":0,"syntology":null},{"url":null,"slug":"computing-sentiment-scores-of-verb-phrases","title":"Computing Sentiment Scores of Verb Phrases for Vietnamese","date":"2016-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"twitter-opinion-topic-model-extracting","title":"Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon","date":"2016-09-21","arxiv_id":"1609.06578","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-model-of-reviews-for-aspect","title":"A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis","date":"2016-09-09","arxiv_id":"1609.02745","repositories_listed":0,"syntology":null},{"url":null,"slug":"keynote-modeling-human-communication-dynamics","title":"Keynote - Modeling Human Communication Dynamics","date":"2016-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sarcastic-soulmates-intimacy-and-irony","title":"Sarcastic Soulmates: Intimacy and irony markers in social media messaging","date":"2016-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-domain-adaptation-regularization-for","title":"A Domain Adaptation Regularization for Denoising Autoencoders","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-suggestions-in-opinionated-texts","title":"A Study of Suggestions in Opinionated Texts and their Automatic Detection","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-argumentative-relation-mining","title":"Context-aware Argumentative Relation Mining","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fill-the-gap-analyzing-implicit-premises","title":"Fill the Gap! Analyzing Implicit Premises between Claims from Online Debates","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-discourse-relation-detection-via-a","title":"Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-polarity-and-implicit-aspect","title":"Implicit Polarity and Implicit Aspect Recognition in Opinion Mining","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-social-norms-evolution-for","title":"Modeling Social Norms Evolution for Personalized Sentiment Classification","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-the-non-substitutability-of","title":"Modeling the Non-Substitutability of Multiword Expressions with Distributional Semantics and a Log-Linear Model","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"suggestion-mining-from-opinionated-text","title":"Suggestion Mining from Opinionated Text","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transductive-adaptation-of-black-box","title":"Transductive Adaptation of Black Box Predictions","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-your-evidence-a-study-of","title":"``What Is Your Evidence?'' A Study of Controversial Topics on Social Media","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"opinion-mining-in-online-reviews-about","title":"Opinion Mining in Online Reviews About Distance Education Programs","date":"2016-07-21","arxiv_id":"1607.06299","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-opinionated-text-for-opinion","title":"Analysis of opinionated text for opinion mining","date":"2016-07-09","arxiv_id":"1607.02576","repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-based-semantic-orientation-of-online","title":"Lexical Based Semantic Orientation of Online Customer Reviews and Blogs","date":"2016-07-08","arxiv_id":"1607.02355","repositories_listed":0,"syntology":null},{"url":null,"slug":"aueb-absa-at-semeval-2016-task-5-ensembles-of","title":"AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"butknot-at-semeval-2016-task-5-supervised","title":"BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-comment-helpfulness-to","title":"Classification of comment helpfulness to improve knowledge sharing among medical practitioners.","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dsic-elirf-at-semeval-2016-task-4-message","title":"DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iit-tuda-at-semeval-2016-task-5-beyond","title":"IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-aspect-detection-in-restaurant","title":"Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inf-ufrgs-opinion-mining-at-semeval-2016-task","title":"INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ju_nlp-at-semeval-2016-task-6-detecting","title":"JU\\_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lys-at-semeval-2016-task-4-exploiting-neural","title":"LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"making-sense-of-massive-amounts-of-scientific","title":"Making Sense of Massive Amounts of Scientific Publications: the Scientific Knowledge Miner Project","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mdsent-at-semeval-2016-task-4-a-supervised","title":"MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nlangp-at-semeval-2016-task-5-improving","title":"NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nru-hse-at-semeval-2016-task-4-comparative","title":"NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"potts-at-semeval-2016-task-4-sentiment","title":"PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentisys-at-semeval-2016-task-5-opinion","title":"SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tgb-at-semeval-2016-task-5-multi-lingual","title":"TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"trainable-citation-enhanced-summarization-of","title":"Trainable Citation-enhanced Summarization of Scientific Articles","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tweester-at-semeval-2016-task-4-sentiment","title":"Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uwb-at-semeval-2016-task-6-stance-detection","title":"UWB at SemEval-2016 Task 6: Stance Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uwb-at-semeval-2016-task-7-novel-method-for","title":"UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dranziera-an-evaluation-protocol-for-multi","title":"DRANZIERA: An Evaluation Protocol For Multi-Domain Opinion Mining","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-data-mining-techniques-for-sentiment","title":"Using Data Mining Techniques for Sentiment Shifter Identification","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-shopping-behavior-study-based-on-multi","title":"Online shopping behavior study based on multi-granularity opinion mining: China vs. America","date":"2016-03-26","arxiv_id":"1603.08089","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-software-quality-from-software-reviews","title":"Mining Software Quality from Software Reviews: Research Trends and Open Issues","date":"2016-02-05","arxiv_id":"1602.02133","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-hadoop-for-large-scale-analysis-on","title":"Using Hadoop for Large Scale Analysis on Twitter: A Technical Report","date":"2016-02-03","arxiv_id":"1602.01248","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-of-twitter-data-a-survey","title":"Sentiment Analysis of Twitter Data: A Survey of Techniques","date":"2016-01-26","arxiv_id":"1601.06971","repositories_listed":0,"syntology":null},{"url":null,"slug":"approaches-for-sentiment-analysis-on-twitter","title":"Approaches for Sentiment Analysis on Twitter: A State-of-Art study","date":"2015-12-03","arxiv_id":"1512.01043","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-feature-selection-and-parameter","title":"Simultaneous Feature Selection and Parameter Optimization Using Multi-objective Optimization for Sentiment Analysis","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-engagement-with-insurgents","title":"Understanding engagement with insurgents through retweet rhetoric","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-skipgrams-bigrams-and-part-of-speech","title":"Using Skipgrams, Bigrams, and Part of Speech Features for Sentiment Classification of Twitter Messages","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-on-youtube-a-brief-survey","title":"Sentiment Analysis on YouTube: A Brief Survey","date":"2015-11-30","arxiv_id":"1511.09142","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-sentiment-prediction-based","title":"Machine Learning Sentiment Prediction based on Hybrid Document Representation","date":"2015-11-29","arxiv_id":"1511.09107","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-issues-of-public-concern-by","title":"運用關聯分析探勘民眾關注議題與發展方向:以環保議題為例(Mining Issues of Public Concern by Association Analysis: Using Environmental Issue as an Example)[In Chinese]","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analyzer-with-rich-features-for","title":"Sentiment Analyzer with Rich Features for Ironic and Sarcastic Tweets","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-classification-of-arabic-documents","title":"Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-a-corpus-of-cantonese-verbal-comments","title":"Toward a Corpus of Cantonese Verbal Comments and their Classification by Multi-dimensional Analysis","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"opinion-mining-from-twitter-data-using","title":"Opinion mining from twitter data using evolutionary multinomial mixture models","date":"2015-09-24","arxiv_id":"1509.07344","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-wordnet-based-sentiment-lexicon-for","title":"A Large Wordnet-based Sentiment Lexicon for Polish","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-lingual-annotated-dataset-for-aspect","title":"A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantitative-analysis-of-gender-differences","title":"A quantitative analysis of gender differences in movies using psycholinguistic normatives","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"connotation-in-translation","title":"Connotation in Translation","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-speculations-contrasts-and","title":"Detecting speculations, contrasts and conditionals in consumer reviews","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-condition-opinion-relations-toward","title":"Extracting Condition-Opinion Relations Toward Fine-grained Opinion Mining","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-opinion-mining-with-recurrent","title":"Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-prediction-in-mst-style-discourse","title":"Joint prediction in MST-style discourse parsing for argumentation mining","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-relationship-between-authors","title":"Learning Relationship between Authors' Activity and Sentiments: A case study of online medical forums","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lost-in-discussion-tracking-opinion-groups-in","title":"Lost in Discussion? Tracking Opinion Groups in Complex Political Discussions by the Example of the FOMC Meeting Transcriptions","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-of-persons-names-in","title":"Named Entity Recognition of Persons' Names in Arabic Tweets","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"optimising-agile-social-media-analysis","title":"Optimising Agile Social Media Analysis","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"phrasernn-phrase-recursive-neural-network-for","title":"PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"squibs-evaluation-methods-for-statistically","title":"Squibs: Evaluation Methods for Statistically Dependent Text","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-lexicon-grammar-based-framework-for","title":"Towards a Lexicon-grammar based Framework for NLP: an Opinion Mining Application","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-opinion-mining-from-reviews-for-the","title":"Towards Opinion Mining from Reviews for the Prediction of Product Rankings","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-extraction-of-customer-to","title":"Towards the Extraction of Customer-to-Customer Suggestions from Reviews","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-topic-specific-domain-dependency","title":"Unsupervised Topic-Specific Domain Dependency Graphs for Aspect Identification in Sentiment Analysis","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-light-lexicon-based-mobile-application-for","title":"A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-linked-data-model-for-multimodal-sentiment","title":"A Linked Data Model for Multimodal Sentiment and Emotion Analysis","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-shallow-discourse-parsing-system-based-on","title":"A Shallow Discourse Parsing System Based On Maximum Entropy Model","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-opinions-cross-lingual-opinion","title":"Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-combined-sentiment-classification-system","title":"An combined sentiment classification system for SIGHAN-8","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-keyword-extraction-and-its","title":"Bilingual Keyword Extraction and its Educational Application","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ct-spa-text-sentiment-polarity-prediction","title":"CT-SPA: Text sentiment polarity prediction model using semi-automatically expanded sentiment lexicon","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-models-for-sentiment-analysis","title":"Deep Learning Models for Sentiment Analysis in Arabic","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-social-relationships-in-face-to","title":"Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-selection-improves-cross-lingual","title":"Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/learning-semantic-representations-of-users","slug":"learning-semantic-representations-of-users","title":"Learning Semantic Representations of Users and Products for Document Level Sentiment Classification","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prior-polarity-lexical-resources-for-the","title":"Prior Polarity Lexical Resources for the Italian Language","date":"2015-07-01","arxiv_id":"1507.00133","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-over-generation-errors-for-automatic","title":"Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-and-belief-how-to-think-about","title":"Sentiment and Belief: How to Think about, Represent, and Annotate Private States","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-aspect-extraction-based-on","title":"Sentiment-Aspect Extraction based on Restricted Boltzmann Machines","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"text-categorization-as-a-graph-classification","title":"Text Categorization as a Graph Classification Problem","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-unitn-discourse-parser-in-conll-2015","title":"The UniTN Discourse Parser in CoNLL 2015 Shared Task: Token-level Sequence Labeling with Argument-specific Models","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"02e7fea3645ab2fd0c3183000897db3200e08214a9dfb33054d6aa2bbe859a42","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}