Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold
By Hassan Saif, Miriam Fernández, Yulan He, Harith Alani. ESSEM@AI*IA, 2013
321 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/bd7669598ec01264ceff8581cb29b1ce8d63b179
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Citations in the corpus
- Transformer models for text-based emotion detection: a review of BERT-based approaches (2021, methodology): “, the semantic text similarity (STS) dataset consisting of 632 positive and 1402 negative tweets (Saif et al. 2013), the Stanford Sentiment Analysis Treebank (SST) dataset containing 4871 positive and 4650 negative movie reviews (Socher et al.”
- Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm (2017, unknown): “Two of the datasets are from SentiStrength (Thel-wall et al., 2010), SS-Twitter and SS-Youtube, and follow the relabeling described in (Saif et al., 2013) to make the labels binary.”
- On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter (2014, unknown): “• The STS-Gold dataset (Saif et al., 2013).”
- Like It or Not (2016, methodology): “A different approach was considered by Hassan et al. [2013], who proposed a boot-strapping ensemble framework.”
- A survey of sentiment analysis in social media (2018, background): “[109] presented an overview of eight publicly available manually annotated evaluation datasets for Twitter sentiment analysis and a common limitation of most of these datasets.”
- BYU-EVE: Mixed Initiative Dialog via Structured Knowledge Graph Traversal and Conversational Scaffolding (2018, background): “Social media dialogues like Twitter [28] or online forums like Reddit [26] avoid the key pitfalls of movie scripts, but also tend to contain highly controversial material, personal insults, and incendiary opinions: not exactly the behaviors we wish our socialbot to emulate.”
- SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis (2020, methodology): “Model Year SST Dataset [53] STS Dataset [50] SemEval-2013 [40] SemEval-2015 [47] SemEval-2016 [39] Sanders [2]”