---
title: "Automatic Identification of Sarcasm Target: An Introductory"
description: "This paper proposes a follow-up task. 24 references, cited by 3 papers here. Among them: 3."
authors: ["Aditya Joshi", "Pranav Goel", "Pushpak Bhattacharyya", "Mark Carman"]
year: 2016
venue: "arXiv:1610.07091"
source: "https://arxiv.org/abs/1610.07091"
doi: "10.48550/arxiv.1610.07091"
arxiv: "1610.07091"
licence: "arXiv non-exclusive distribution licence (read only, no redistribution)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/joshi-2016-automatic-identification-sarcasm-target-introductory/"
---
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# Automatic Identification of Sarcasm Target: An Introductory Approach

**Authors:** [Aditya Joshi](https://md.research.elamur.ai/free/en/authors/aditya-joshi/), [Pranav Goel](https://md.research.elamur.ai/free/en/authors/pranav-goel/), [Pushpak Bhattacharyya](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) and [Mark Carman](https://md.research.elamur.ai/free/en/authors/mark-carman/) · **Year:** 2016 · **Venue:** arXiv:1610.07091 · **Licence:** [arXiv non-exclusive distribution licence (read only, no redistribution)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1610.07091) · [PDF](https://arxiv.org/pdf/1610.07091) · [DOI 10.48550/arxiv.1610.07091](https://doi.org/10.48550/arxiv.1610.07091) · [arXiv 1610.07091](https://arxiv.org/abs/1610.07091) · [OpenAlex](https://openalex.org/W2544245958)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-identification-sarcasm-target-introductory/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most computational sarcasm research stops once it decides whether a text is sarcastic. This paper proposes a follow-up task. Given a text already known to be sarcastic, the system must find the words that name the thing being mocked. If the mocked entity never appears in the text, it returns the fallback label 'Outside'.

The authors build a hybrid system with three parts:
- **Rule-based extractor.** Nine hand-written rules cover cues such as pronouns, named entities, sentiment contrasts around verbs, similes and demonstratives. Their votes are merged, with each rule weighted by its measured accuracy.
- **Statistical extractor.** Every word becomes a binary instance, classified with lexical, POS and sentiment features.
- **Integrator.** It joins the two outputs by OR or by AND.

The data are manually annotated book snippets and tweets, scored with Exact Match and Dice Score. The OR variant beats a naive baseline, a baseline from sentiment target identification, and either extractor alone. Its best result is a Dice Score of 39.63 on tweets. The task matters because it lets aspect-based sentiment analysis attach sarcastic negativity to the correct aspect.

## Key points
- The paper claims to be the first to define sarcasm target identification. The output is either a subset of the sentence's words or 'Outside'; one sentence can have several targets.
- The nine rules R1–R9 cover:
  - pronouns and pronominal adjectives;
  - named entities;
  - a sentiment-bearing verb as the pivot, and a non-sentiment-bearing verb as the pivot (both after Riloff et al. 2013);
  - gerunds and infinitives;
  - positive adjective + noun;
  - interrogatives;
  - similes (after Veale and Hao 2010);
  - demonstrative adjectives.
- The statistical extractor splits a sentence of n words into n labelled instances. If the target lies outside the text, every word gets label 0.
- Hybrid OR is the best configuration:
  - book snippets: EM 7.01, DS 32.68;
  - tweets: EM 9.09, DS 39.63.
- 'Outside' cases make up about 5% of book snippets (11 texts) and about 10% of tweets (53). On these cases DS falls to 6.81 for books and 4.71 for tweets.

## Interesting ideas
- An explicit 'Outside' label lets the system admit that the mocked entity is implied rather than spoken, so it does not force a wrong word into the answer.
- Rule outputs are combined by summing, for each word, the standalone accuracy of every rule that selected it; the highest-scoring words win. This is a cheap, transparent alternative to a learned combiner.
- R3 and R4 locate the target through verb structure. A positive-sentiment verb points to its object as the mocked situation; with a neutral verb, the argument with the lower sentiment score is chosen.
- The simile rule returns the subjects on both sides of a comparison, treating several simultaneous targets as normal.
- Scoring the 'Outside' slice separately revealed that partial-match credit collapses there, so Dice equals Exact Match by construction.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): the irony module could bind the target to an unspoken discourse referent, such as the addressee or the situation, instead of always choosing a token.
- **I2** (learn): self-induced repair rules or witness parsers could vote with weights equal to their measured precision, so every decision traces back to accountable sources.
- **I3** (frames): applied over UD relations like obj/xcomp/nsubj, this gives an explainable irony detector grounded in the frame's roles; the paper itself names syntactic dependencies as future work.
- **I4** (world): ironic comparisons could attach a negative evaluation to every compared entity, not only the grammatical subject.
- **I5** (learn): evaluation should report implicit-referent cases as their own slice, so overall partial-match scores cannot hide failures on them.

*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

- [Monash University](https://md.research.elamur.ai/free/en/universities/monash-university/) — University, Melbourne, Australia · 31 works · 54,201 citations
- [Indian Institute of Technology Bombay](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-bombay/) — University, Mumbai, India · 22 works · 2,018 citations
- [Indian Institute of Technology BHU](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-bhu/) — University, Varanasi, India · 6 works · 525 citations

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🧭 Topics (1): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 608 works

## Cite

Cite as: Aditya Joshi, Pranav Goel, Pushpak Bhattacharyya and Mark Carman. 2016. Automatic Identification of Sarcasm Target: An Introductory Approach. arXiv:1610.07091.

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

```bibtex
@misc{joshi2016automatic,
  title = {{Automatic Identification of Sarcasm Target: An Introductory Approach}},
  author = {Aditya Joshi, Pranav Goel, Pushpak Bhattacharyya and Mark Carman},
  year = {2016},
  doi = {10.48550/arxiv.1610.07091},
  eprint = {1610.07091},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1610.07091}
}
```

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## References

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*Updated 24 October 2026*