Corpus ID: 12233345

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

@inproceedings{Hutto2014VADERAP,
  title={VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},
  author={Clayton J. Hutto and Eric Gilbert},
  booktitle={ICWSM},
  year={2014}
}
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.Expand
LingoSent - A Platform for Linguistic Aware Sentiment Analysis for Social Media Messages
TLDR
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. Expand
Fuzzy rule based unsupervised sentiment analysis from social media posts
TLDR
The proposed fuzzy system integrates Natural Language Processing techniques and Word Sense Disambiguation using a novel unsupervised nine fuzzy rule based system to classify the post into: positive, negative or neutral sentiment class. Expand
Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions
TLDR
The results show that the most recent DL language modeling approach provides the highest quality; however, this quality comes at reduced model transparency. Expand
A Fuzzy Logic Inspired Approach for Social Media Sentiment Analysis via Deep Neural Network
TLDR
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. Expand
Sentiment Analysis Using Part-of-Speech-Based Feature Extraction and Game-Theoretic Rough Sets
Sentiment analysis, one of the most trending natural language processing tasks, is used to mine opinions or sentiments from a given text. Two significant challenges of sentiment analysis are 1)Expand
Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs
TLDR
It is shown that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts and can aid understanding of texts with reliable and meaningful word shift graphs if the dictionary covers a sufficiently large portion of a given text’s lexicon when weighted by word usage frequency. Expand
SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints
TLDR
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. Expand
Sentiment Evaluation: User, Business Assessment and Hashtag Analysis
TLDR
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. Expand
Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons
TLDR
To complement traditional sentiment dictionaries, this work presents a system for lexicon expansion that extracts the most relevant terms from news and assesses their positive or negative score through Twitter, and shows that complementary lexicons increase the performance of three state-of-the-art sentiment systems. Expand
Cross-Domain Sentiment Analysis on Social Media Interactions using Senti-Lexicon based Hybrid Features
TLDR
Experiments show that hybridizing the BoW, linguistic N-Gram and POS method with lexicon features improves the accuracy of sentiment classification even for cross-domain sentiment analysis. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 49 REFERENCES
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
TLDR
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. Expand
Automatic construction of a context-aware sentiment lexicon: an optimization approach
TLDR
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. Expand
A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs
TLDR
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. Expand
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
TLDR
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. Expand
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
TLDR
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. Expand
Sentiment Extraction: Integrating Statistical Parsing, Semantic Analysis, and Common Sense Reasoning
TLDR
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. Expand
Opinion Mining and Sentiment Analysis
TLDR
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. Expand
A holistic lexicon-based approach to opinion mining
TLDR
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. Expand
Sentiment Analysis and Opinion Mining
  • B. Liu
  • Computer Science
  • Synthesis Lectures on Human Language Technologies
  • 2012
This 2012 book is written as a comprehensive introductory and survey text for sentiment analysis and opinion mining, a field of study that investigates computational techniques for analyzing text toExpand
SenticNet: A Publicly Available Semantic Resource for Opinion Mining
TLDR
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. Expand
...
1
2
3
4
5
...