---
title: "Love Me, Love Me Not — paper by Israeli et al. (2022)"
description: "The paper introduces human-directed sentiment analysis (HD-Sentiment). 47 references, cited by 1 paper here."
authors: ["Abraham Israeli", "Aviv Naaman", "Yotam Nahum", "Razan Assi", "Shai Fine", "Kfir Bar"]
year: 2022
venue: "Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022"
source: "https://aclanthology.org/2022.nsurl-1.4/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/israeli-2022-love-me-love-me-not/"
---
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# Love Me, Love Me Not: Human-Directed Sentiment Analysis in Arabic

**Authors:** [Abraham Israeli](https://md.research.elamur.ai/free/en/authors/abraham-israeli/), Aviv Naaman, [Yotam Nahum](https://md.research.elamur.ai/free/en/authors/yotam-nahum/), Razan Assi, [Shai Fine](https://md.research.elamur.ai/free/en/authors/shai-fine/) and [Kfir Bar](https://md.research.elamur.ai/free/en/authors/kfir-bar/) · **Year:** 2022 · **Venue:** [Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022](https://md.research.elamur.ai/free/en/venues/nsurl/) ([International Workshop on NLP Solutions for Under Resourced Languages co-located with ICNLSP - Short Papers 2022](https://md.research.elamur.ai/free/en/venues/nsurl/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.nsurl-1.4/) · [PDF](https://aclanthology.org/2022.nsurl-1.4.pdf)

**Citations:** 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper introduces *human-directed sentiment analysis* (HD-Sentiment). The task asks what attitude a person replying in a discussion thread holds toward the author of the original post. This differs from the reply's overall mood. The authors treat it as a special case of ABSA in which the only "aspect" is the main post's author.

They built a corpus from Twitter. They collected threads through 61 Arabic search expressions and a list of high-engagement accounts, focusing on Middle Arabic that mixes MSA with Levantine dialects. They then sampled 3,500 post–reply pairs and hand-labelled each as positive, neutral or negative toward the original poster.

Five-fold cross-validation compared five systems:
- a lexicon method,
- an off-the-shelf CAMeLBERT sentiment classifier,
- fine-tuned AraBERT,
- fine-tuned GigaBERT,
- fine-tuned MARBERT.

MARBERT, pretrained on tweets, performed best, with 0.82 weighted-F1. The released data and model support work on hate speech, cyber-bullying and relations between users in dialogue. These areas are underserved for Arabic.

## Key points
- The collection gathered 20.1K threads (346.3K tweets) in June 2021 via full-archive crawling. The annotated release has 3.5K main-post/response pairs.
- MARBERT reached 0.79 macro-F1 and 0.82 weighted-F1. It was followed by GigaBERT (0.66 / 0.75), AraBERT (0.57 / 0.69), CAMeLBERT-DA sentiment (0.46 / 0.45) and the lexicon baseline (0.40 / 0.48). Significance was checked with a Mann-Whitney U-test.
- Positive was the hardest and least frequent label, with an F1 of 0.72 even for MARBERT. Most of MARBERT's confusions were between neutral and negative, while positive and negative were rarely swapped.
- MARBERT misclassified 602 pairs. Of these, 52.7% had received conflicting labels from the original annotators, double the corpus-wide disagreement rate of 26%.
- The corpus, keyword list and account list are on GitHub (idc-dsi/Human-Directed-Sentiment), and the model is on HuggingFace (DSI/human-directed-sentiment).

## Interesting ideas
- The paper redefines sentiment as an attitude held by one participant toward another specific participant, rather than a property of the whole text, and sarcastic or implicit replies make the difference visible.
- The lexicon baseline first checks whether the reply refers to the original author at all, including through 2nd-person pronouns, and outputs neutral when no such reference exists. Only then does it score emotional words.
- A generic whole-text sentiment model is included as a baseline, and its weak score shows that targeted sentiment cannot be recovered from overall tone.
- Model errors are cross-checked against human annotator disagreement, which separates genuinely ambiguous items from real model failures.
- The task deliberately restricts input to a single post–reply pair instead of the full thread, which keeps the problem small enough to annotate and model.

## For Mova

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

- **I1** (frames): prag could add a stance slot to each reply frame, of the form "speaker → addressee: positive / neutral / negative", which stays separate from the polarity of the propositions mentioned.
- **I2** (frames): gating attitude attribution on resolving "you" or a mention to a known participant would make every stance decision traceable to an explicit coreference link.
- **I3** (learn): an untargeted "overall polarity" scorer could serve as a negative control, so that any targeted-stance component must clearly beat it to count as working.
- **I4** (learn): when grading against silver or teacher labels, items on which witnesses disagree could be flagged and reported separately, so repair rules are not induced from inherently ambiguous cases.
- **I5** (world): the world of a text could start with minimal two-turn exchanges, with participants and their attitudes as entity states, before scaling up to whole conversations.

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

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works

🧭 Topics (2): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 972 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works

## Cite

Cite as: Abraham Israeli, Aviv Naaman, Yotam Nahum, Razan Assi, Shai Fine and Kfir Bar. 2022. Love Me, Love Me Not: Human-Directed Sentiment Analysis in Arabic. Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022.

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

```bibtex
@inproceedings{israeli2022love,
  title = {{Love Me, Love Me Not: Human-Directed Sentiment Analysis in Arabic}},
  author = {Abraham Israeli, Aviv Naaman, Yotam Nahum, Razan Assi, Shai Fine and Kfir Bar},
  booktitle = {Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022},
  year = {2022},
  url = {https://aclanthology.org/2022.nsurl-1.4/}
}
```

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## References

47 references, 19 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 1 November 2026*