# Curating corpora with classifiers: A case study of clean energy sentiment online

arXiv: https://arxiv.org/abs/2305.03092 · HTML: https://mlsanity.com/paper/2305.03092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 75 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Human language reveals a universal positivity bias](https://mlsanity.com/paper/1406.3855/) (2014)
- [Twitter Topic Classification](https://mlsanity.com/paper/2209.09824/) (2022)
- [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://mlsanity.com/paper/2008.02250/) (2020)
- [Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter](https://mlsanity.com/paper/2007.12988/) (2020)
- [Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter](https://mlsanity.com/paper/1606.06820/) (2016)
- [Climate change sentiment on Twitter: An unsolicited public opinion poll](https://mlsanity.com/paper/1505.03804/) (2015)
- [Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language Model](https://mlsanity.com/paper/2007.13306/) (2020)
- [How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter](https://mlsanity.com/paper/2003.12614/) (2020)
- [The Lexicocalorimeter: Gauging public health through caloric input and output on social media](https://mlsanity.com/paper/1507.05098/) (2015)
- [Public Opinion Polling with Twitter](https://mlsanity.com/paper/1608.02024/) (2016)
- [Say Their Names: Resurgence in the collective attention toward Black victims of fatal police violence following the death of George Floyd](https://mlsanity.com/paper/2106.10281/) (2021)
- [English verb regularization in books and tweets](https://mlsanity.com/paper/1803.09745/) (2018)
- [Hurricanes and hashtags: Characterizing online collective attention for natural disasters](https://mlsanity.com/paper/2003.14291/) (2020)
- [How Do US Congress Members Advertise Climate Change: An Analysis Of Ads Run On Meta's Platforms](https://mlsanity.com/paper/2304.03278/) (2023)

## Abstract (arXiv)

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries.