---
title: "Exploring Demographic Language Variations to Improve"
description: "The authors study English, Spanish and Russian. 65 references, cited by 68 papers here."
authors: ["Svitlana Volkova", "Theresa Wilson", "David Yarowsky"]
year: 2013
venue: "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D13-1187/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/volkova-2013-exploring-demographic-language-variations-improve/"
---
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# Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media

**Authors:** [Svitlana Volkova](https://md.research.elamur.ai/free/en/authors/svitlana-volkova/), [Theresa Wilson](https://md.research.elamur.ai/free/en/authors/theresa-wilson/) and [David Yarowsky](https://md.research.elamur.ai/free/en/authors/david-yarowsky/) · **Year:** 2013 · **Venue:** [Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2013](https://md.research.elamur.ai/free/en/venues/emnlp/2013/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D13-1187/) · [PDF](https://aclanthology.org/D13-1187.pdf) · [OpenAlex](https://openalex.org/W2251409655)

**Citations:** 182 citations (OpenAlex, 2026-10-14) · [68](https://md.research.elamur.ai/free/en/papers/volkova-2013-exploring-demographic-language-variations-improve/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper asks whether men and women express opinions differently on Twitter, and whether a sentiment classifier can use those differences. The authors study English, Spanish and Russian. For each language they use about 800K tweets with gender labels but no sentiment labels. They also have 2K development and 2K test tweets annotated for both gender and sentiment through Amazon Mechanical Turk.

From the large unlabeled pool they bootstrap Twitter-specific subjectivity lexicons with a corpus-based, language-independent method. They then derive gender-dependent sentiment terms and compare how the genders use hashtags and emoticons across the three cultures. These terms become set-count features alongside ordinary lexical features.

Gender-dependent models consistently beat gender-independent baselines. The gains are statistically significant, and polarity classification improves in all three languages. A plain binary "author gender" feature does not reproduce the effect. The work suggests that demographic signals can improve opinion mining, especially for languages with fewer resources than English.

## Key points
- **Data:**
  - English tweets come from the corpus of Burger et al. (2011): 2,958,103 tweets from 184K users. Retweets were removed, and a gender-balanced 0.8M sample of English tweets was drawn.
  - Russian and Spanish tweets were collected from followers of regional news and media accounts. Gender was assigned from first- and last-name morphology with precision above 0.98.
- **Annotation:**
  - Sentiment labels cover four classes: positive, negative, both and neutral.
  - Each tweet was labeled by five MTurk workers and the majority vote was kept.
  - Language identification used the compression-based LID tool of Bergsma et al. (2012).
- **Gains:** The abstract reports relative F-measure improvements for polarity and subjectivity respectively:

  | Language | Polarity | Subjectivity |
  |---|---|---|
  | Russian | 1.5% | 1% |
  | Spanish | 2% | 0.5% |
  | English | 2.5% | 5% |

  The conclusion section words these figures somewhat differently.
- **Significance and robustness:**
  - Significance was tested with McNemar's test (p \< 0.01).
  - The best results came from Logistic Regression with joint features and SVM with an RBF kernel with disjoint features. BLR reached an F-measure of 82% on English polarity.
  - Only J48, AdaBoostM1 and Random Forest failed to improve significantly in every setting.
- **Resources and examples:**
  - The gender-dependent and gender-independent lexicons for all three languages were released publicly.
  - Example of gender-flipped polarity: English *weakness* tends to be positive for women and negative for men. Russian *достичь* ("achieve") is positive for men and negative for women.

## Interesting ideas
- **Lexicon bootstrapping without rich resources.** Tweets are labeled with the current lexicon, the most subjectivity-associated new words are added, and the loop repeats until nothing new qualifies. Parameters are tuned on a small labeled dev set. Since it needs no WordNet or parser, this could be applied to Ukrainian or other under-resourced social media text.
- **Permutation sanity check.** The authors randomly shuffle which terms count as "male" or "female" and confirm that accuracy drops. This cheap control shows that a demographic feature carries real signal rather than just adding capacity, and it suits any feature built from group-specific word lists.
- **Group-specific lexicons over a group flag.** A binary gender indicator helped little. Counting hits from gender-specific sentiment term sets, combined with lexical features, did help. The same design could encode region, age or dialect.
- **Morphology as a free demographic label.** For languages with gendered name forms, such as Russian (and similarly Ukrainian), author gender can be inferred cheaply and with high precision. This makes large weakly labeled corpora possible.
- **Grammatical gender may absorb the demographic signal.** The authors suggest that subjectivity gains were smaller for Spanish and Russian because the words themselves are already inflected for gender. This is worth keeping in mind when transferring such methods to morphologically rich languages.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Johns Hopkins University](https://md.research.elamur.ai/free/en/universities/johns-hopkins-university/) — University, Baltimore, United States · 187 works · 92,194 citations

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## Cite

Cite as: Svitlana Volkova, Theresa Wilson and David Yarowsky. 2013. Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{volkova2013exploring,
  title = {{Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media}},
  author = {Svitlana Volkova, Theresa Wilson and David Yarowsky},
  booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
  year = {2013},
  doi = {10.18653/v1/d13-1187},
  url = {https://doi.org/10.18653/v1/d13-1187}
}
```

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## References

65 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*