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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A Sequential Algorithm for Training Text Classifiers
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Intrinsic Evaluation of Lithuanian Word Embeddings Using WordNet
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Combining Vector Space Features and Convolution Neural Network for Text Sentiment Analysis
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Improving Citation Sentiment and Purpose Classification Using Hybrid Deep Neural Network Model
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Sentiment Analysis of Lithuanian Texts Using Deep Learning Methods
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Supervised Sequence Labelling with Recurrent Neural Networks
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Evaluation for Teacher’s Ability and Forecasting Student’s Career Based on Big Data
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Tweets Sentiment Analysis for Healthcare on Big Data Processing and IoT Architecture Using Maximum Entropy Classifier
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Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages
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Text categorization with Support Vector Machines: Learning with many relevant features
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Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis
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A visual framework for dynamic emotional web analysis
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Gradient-based learning applied to document recognition
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Twitter for Sentiment Analysis: When Language Resources are Not Available
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Twitter Sentiment Analysis with Deep Convolutional Neural Networks
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Intelligent Topical Sentiment Analysis for the Classification of E‐Learners and Their Topics of Interest
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Lexicon-Based Methods for Sentiment Analysis
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Sentiment processing of social media information from both wireless and wired network
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Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics
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Lexicon-enhanced sentiment analysis framework using rule-based classification scheme
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Link prediction on Twitter
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
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Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis
inferredcites →https://doi.org/10.18653/v1/s17-2088openalex-snapshot
SemEval-2017 Task 4: Sentiment Analysis in Twitter
inferredcites →https://doi.org/10.18653/v1/s17-2089openalex-snapshot
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
inferredcites →https://doi.org/10.18653/v1/s17-2094openalex-snapshot
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
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DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis
inferredcites →https://doi.org/10.18653/v1/s17-2152openalex-snapshot
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
inferredcites →https://doi.org/10.18653/v1/s17-2154openalex-snapshot
IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis
inferredcites →https://doi.org/10.18653/v1/w17-1411openalex-snapshot
Comparison of Short-Text Sentiment Analysis Methods for Croatian
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Using emoticons to reduce dependency in machine learning techniques for sentiment classification
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Convolutional Neural Networks for Sentence Classification
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Understanding Citizen Issues through Reviews: A Step towards Data Informed Planning in Smart Cities
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Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis
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Distributed Representations of Words and Phrases and their Compositionality
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Character-Based Machine Learning vs. Language Modeling for Diacritics Restoration
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Learning Word Vectors for 157 Languages
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Quantifying the effect of sentiment on information diffusion in social media
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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