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Opinion Mining

Identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)

Image Source: Deep learning for sentiment analysis: A survey

Papers

Showing 151–200 of 630 papers

TitleStatusHype
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining—0
Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content—0
Confirmatory Aspect-based Opinion Mining Processes—0
Advances in Argument Mining—0
Citizens' Emotion on GST: A Spatio-Temporal Analysis over Twitter Data—0
OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features—0
YNU-HPCC at SemEval-2019 Task 9: Using a BERT and CNN-BiLSTM-GRU Model for Suggestion Mining—0
Opinion Mining with Deep Contextualized Embeddings—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role LabelingCode0
SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data—0
Using Entity Relations for Opinion Mining of Vietnamese Comments—0
Machine Learning based English Sentiment Analysis—0
Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only—0
A multimodal movie review corpus for fine-grained opinion miningCode0
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications—0
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining—0
Emotion Detection and Analysis on Social Media—0
Supervised Sentiment Classification with CNNs for Diverse SE DatasetsCode0
Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features—0
Visualizing Group Dynamics based on Multiparty Meeting Understanding—0
Normalization of Transliterated Words in Code-Mixed Data Using Seq2Seq Model \& Levenshtein Distance—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks—0
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification—0
Sentiment Classification towards Question-Answering with Hierarchical Matching Network—0
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep LearningCode0
Arabizi sentiment analysis based on transliteration and automatic corpus annotation—0
EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit EmotionsCode0
Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification—0
Argumentation Mining: Exploiting Multiple Sources and Background Knowledge—0
Multi-label Classification of User Reactions in Online News—0
Stance Detection with Hierarchical Attention Network—0
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous VehiclesCode0
Multimodal Named Entity Disambiguation for Noisy Social Media Posts—0
Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection—0
Framework for Opinion Mining Approach to Augment Education System Performance—0
Addition of Code Mixed Features to Enhance the Sentiment Prediction of Song Lyrics—0
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic—0
Nested Named Entity Recognition Revisited—0
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection—0
A Fine-grained Large-scale Analysis of Coreference Projection—0
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron—0
Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset—0
Irony Detector at SemEval-2018 Task 3: Irony Detection in English Tweets using Word Graph—0
TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction—0
CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities—0
CENNLP at SemEval-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction Representation—0
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm DetectionCode0
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