VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text

  title={VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},
  author={Clayton J. Hutto and Eric Gilbert},
  journal={Proceedings of the International AAAI Conference on Web and Social Media},
  • C. HuttoEric Gilbert
  • Published 16 May 2014
  • Computer Science
  • Proceedings of the International AAAI Conference on Web and Social Media
The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. [] Key Method Using a combination of qualitative and quantitative methods, we first construct and empirically validate a goldstandard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts.

Figures and Tables from this paper

LingoSent - A Platform for Linguistic Aware Sentiment Analysis for Social Media Messages

This article designs the sentiment analysis approach as a framework with data preprocessing, linguistic feature extraction and sentiment calculation being separate components and shows that the system outperforms existing state-of-the-art lexicon-based sentiment analysis solutions.

A Fuzzy Logic Inspired Approach for Social Media Sentiment Analysis via Deep Neural Network

An efficient method of classification of sentiment in social media texts, each consisting of single or multiple sentence(s) that most of the time includes pop culture texts, using recurrent fuzzy neural network and Recursive Neural Network is presented.

Sentiment Analysis Using Part-of-Speech-Based Feature Extraction and Game-Theoretic Rough Sets

A model using part-of-speech-based feature extraction to reduce dimensionality and game-theoretic rough sets (GTRS) to establish a balance between the accuracy and coverage trade-off is proposed to deal with the complexity and uncertainty in sentiment analysis tasks.

SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints

SenHint is presented, which integrates the output of deep neural networks and the implication of linguistic hints into a coherent reasoning model based on Markov Logic Network (MLN), and can effectively improve accuracy compared with the state-of-the-art alternatives.

To Scale or Not to Scale: Comparing Popular Sentiment Analysis Dictionaries on Educational Twitter Data

Researchers are encouraged to consider employing overall sentiment scales or positive/neutral to negative ratios based on binary classification to characterize their sample, and be aware of the current limitations of detecting negativity through dictionary-based sentiment analysis in educational contexts.

A Generalized Method for Sentiment Analysis across Different Sources

  • A. Ashir
  • Computer Science
    Appl. Comput. Intell. Soft Comput.
  • 2021
Experimental results with different machine leaning classifiers indicate that improved performance with great deal of generalization capacity across both structured and nonstructured sources can be realized.

Sentiment Evaluation: User, Business Assessment and Hashtag Analysis

The methods described here provide users with a robust and flexible way of profiling Twitter users using sentiment extracted from tweet data, and show that Artificial Neural Networks perform the best with 76.46% accuracy.

Sentiment analysis in tweets: an assessment study from classical to modern word representation models

This study fulfills an assessment of existing neural language models in distinguishing the sentiment expressed in tweets, by using a rich collection of 22 datasets from distinct domains and five classification algorithms.



Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

A supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service, is proposed, utilizing 50 Twitter tags and 15 smileys as sentiment labels, allowing identification and classification of diverse sentiment types of short texts.

Automatic construction of a context-aware sentiment lexicon: an optimization approach

This paper proposes a novel optimization framework that provides a unified and principled way to combine different sources of information for learning a context-dependent sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection.

A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs

This work wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison with a new word list specifically constructed for microblogs.

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

A novel machine-learning method is proposed that applies text-categorization techniques to just the subjective portions of the document, which greatly facilitates incorporation of cross-sentence contextual constraints.

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.

Sentiment Extraction: Integrating Statistical Parsing, Semantic Analysis, and Common Sense Reasoning

iSEE is presented, a fully implemented sentiment extraction engine, which makes use of statistical methods, classical NLU techniques, common sense reasoning, and probabilistic inference to extract entity and feature specific sentiment from complex sentences and dialog.

A holistic lexicon-based approach to opinion mining

This paper proposes a holistic lexicon-based approach to solving the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews by exploiting external evidences and linguistic conventions of natural language expressions.

Sentiment Analysis and Opinion Mining

  • Lei ZhangB. Liu
  • Computer Science
    Encyclopedia of Machine Learning and Data Mining
  • 2012
This book is a comprehensive introductory and survey text that covers all important topics and the latest developments in the field with over 400 references and is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular.

SenticNet: A Publicly Available Semantic Resource for Opinion Mining

SenticNet is a publicly available resource for opinion mining built exploiting AI and Semantic Web techniques and uses dimensionality reduction to infer the polarity of common sense concepts and hence provide a public resource for mining opinions from natural language text at a semantic, rather than just syntactic, level.

Subjectivity Word Sense Disambiguation

This talk will give the results of a study showing that even words judged in previous work to be reliable opinion clues have significant degrees of subjectivity sense ambiguity, and evidence that SWSD is more feasible than full word sense disambiguation because it is more coarse grained.