Semantic Sentiment Analysis of Twitter

By Hassan Saif, Yulan He, Harith Alani. International Workshop on the Semantic Web, 2012

579 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/01f3fdbc51a44f4c39c23580f766375851fa40b8

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Citations in the corpus

Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold (2013, methodology): “The HCR dataset has been used to evaluate polarity classification [23,21] but can also be used to evaluate subjectivity classification since it identifies neutral tweets.”
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter (2014, background): “This phenomena increases the level of data sparsity, affecting the performance of Twitter sentiment classifiers (Saif et al., 2012a).”
Contextual semantics for sentiment analysis of Twitter (2016, methodology): “…Frank, 2010), Part-Of-Speech (POS) tags (Barbosa and Feng, 2010; Agarwal et al., 2011) with or without words’ prior sentiment, words’ semantic concepts (Saif et al., 2012b), sentiment-topic features (Saif et al., 2012a), semantic patterns (Saif et al., 2014d) and tweets syntax features (e.g,…”
Twitter Sentiment Analysis (2015, unknown): “Saiff et al. achieved improvements by growing the feature space with semantics features (Saif et al., 2012).”
Like It or Not (2016, methodology): “For example, Saif et al. [2012b] leveraged the entities related to the terms used in tweets and their sentiment to improve the performance of TSA.”
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory (2015, background): “Saif et al. (2012a; 2012b) make use of sentiment-topic features and entities extracted by a third-party service to ease data sparsity.”
Deep Convolution Neural Networks for Twitter Sentiment Analysis (2018, methodology): “RELATED WORK Most existing studies to Twitter sentiment analysis can be divided into supervised methods [3]–[8] and lexicon-based methods [9]–[11].”
Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis (2017, methodology): “For example, the use of linguistic information such as morphology [7], [8], [9], syntax [8], [9], semantics [10], [11], and lexicons [7], [9] was employed in text representation.”