--- pretty_name: "SENTiVENT Bench: Economic Events and Implicit Sentiment in Financial News" language: - en license: cc-by-4.0 annotations_creators: - expert-generated language_creators: - found language_details: en-US multilinguality: - monolingual size_categories: - n<1K task_categories: - token-classification - text-classification - text-generation task_ids: - sentiment-analysis - sentiment-classification - multi-label-classification - named-entity-recognition - coreference-resolution tags: - event-extraction - sentiment-analysis - financial-news - information-extraction - absa - factuality - financial-sentiment - relation-extraction - financial-nlp - structured-output - aspect-based-sentiment-analysis - event-detection configs: - config_name: sentivent_unified_document default: true data_files: - split: train path: data/sentivent_unified_document/train.parquet - split: validation path: data/sentivent_unified_document/validation.parquet - split: test path: data/sentivent_unified_document/test.parquet - config_name: sentivent_unified_document_continuous data_files: - split: train path: data/sentivent_unified_document_continuous/train.parquet - split: validation path: data/sentivent_unified_document_continuous/validation.parquet - split: test path: data/sentivent_unified_document_continuous/test.parquet - config_name: sentivent_unified_document_with_canonical_referents data_files: - split: train path: data/sentivent_unified_document_with_canonical_referents/train.parquet - split: validation path: data/sentivent_unified_document_with_canonical_referents/validation.parquet - split: test path: data/sentivent_unified_document_with_canonical_referents/test.parquet - config_name: sentivent_unified_sentence data_files: - split: train path: data/sentivent_unified_sentence/train.parquet - split: validation path: data/sentivent_unified_sentence/validation.parquet - split: test path: data/sentivent_unified_sentence/test.parquet - config_name: sentivent_unified_sentence_continuous data_files: - split: train path: data/sentivent_unified_sentence_continuous/train.parquet - split: validation path: data/sentivent_unified_sentence_continuous/validation.parquet - split: test path: data/sentivent_unified_sentence_continuous/test.parquet - config_name: sentivent_unified_sentence_with_canonical_referents data_files: - split: train path: data/sentivent_unified_sentence_with_canonical_referents/train.parquet - split: validation path: data/sentivent_unified_sentence_with_canonical_referents/validation.parquet - split: test path: data/sentivent_unified_sentence_with_canonical_referents/test.parquet - config_name: sentivent_unified_bio_sentence data_files: - split: train path: data/sentivent_unified_bio_sentence/train.parquet - split: validation path: data/sentivent_unified_bio_sentence/validation.parquet - split: test path: data/sentivent_unified_bio_sentence/test.parquet - config_name: ere_textee_sentence_e2e data_files: - split: train path: data/ere_textee_sentence_e2e/train.parquet - split: validation path: data/ere_textee_sentence_e2e/validation.parquet - split: test path: data/ere_textee_sentence_e2e/test.parquet - config_name: ere_textee_sentence_eae data_files: - split: train path: data/ere_textee_sentence_eae/train.parquet - split: validation path: data/ere_textee_sentence_eae/validation.parquet - split: test path: data/ere_textee_sentence_eae/test.parquet - config_name: ere_textee_sentence_ed data_files: - split: train path: data/ere_textee_sentence_ed/train.parquet - split: validation path: data/ere_textee_sentence_ed/validation.parquet - split: test path: data/ere_textee_sentence_ed/test.parquet - config_name: iabsa_asqp_sentence data_files: - split: train path: data/iabsa_asqp_sentence/train.parquet - split: validation path: data/iabsa_asqp_sentence/validation.parquet - split: test path: data/iabsa_asqp_sentence/test.parquet - config_name: iabsa_aste_sentence data_files: - split: train path: data/iabsa_aste_sentence/train.parquet - split: validation path: data/iabsa_aste_sentence/validation.parquet - split: test path: data/iabsa_aste_sentence/test.parquet - config_name: iabsa_quad_sentence data_files: - split: train path: data/iabsa_quad_sentence/train.parquet - split: validation path: data/iabsa_quad_sentence/validation.parquet - split: test path: data/iabsa_quad_sentence/test.parquet - config_name: ere_dygiepp_document data_files: - split: train path: data/ere_dygiepp_document/train.parquet - split: validation path: data/ere_dygiepp_document/validation.parquet - split: test path: data/ere_dygiepp_document/test.parquet - config_name: sentivent_unified_uie_sentence data_files: - split: train path: data/sentivent_unified_uie_sentence/train.parquet - split: validation path: data/sentivent_unified_uie_sentence/validation.parquet - split: test path: data/sentivent_unified_uie_sentence/test.parquet - config_name: ere_omnievent_sentence data_files: - split: train path: data/ere_omnievent_sentence/train.parquet - split: validation path: data/ere_omnievent_sentence/validation.parquet - split: test path: data/ere_omnievent_sentence/test.parquet - config_name: sentivent_unified_oneie_sentence data_files: - split: train path: data/sentivent_unified_oneie_sentence/train.parquet - split: validation path: data/sentivent_unified_oneie_sentence/validation.parquet - split: test path: data/sentivent_unified_oneie_sentence/test.parquet - config_name: ere_oneie_sentence data_files: - split: train path: data/ere_oneie_sentence/train.parquet - split: validation path: data/ere_oneie_sentence/validation.parquet - split: test path: data/ere_oneie_sentence/test.parquet --- # SENTiVENT Bench: Economic Events and Implicit Sentiment in Financial News ![SENTiVENT annotation example](./assets/sentivent-annotation-example.png) SENTiVENT is an expert human-annotated financial news dataset for research on what happens in company news and what sentiment those events imply. It supports financial event extraction, implicit financial sentiment analysis, aspect-based sentiment analysis (ABSA), and structured-output evaluation for ML systems and AI agents. Here, implicit sentiment means investor-relevant polarity inferred from factual news through common sense and world knowledge, not only explicit opinion wording. The resource is designed to be representative of the business-news genre: article selection avoids over-representing one company, sector, topic, or news event, and excludes robo-written or templated articles. ## How To Use For most users and AI evaluation agents, start with `sentivent_unified_document`: `text` is the input, and `annotations` plus `relations` are the gold structured target. ```python import evaluate from datasets import load_dataset test = load_dataset("GillesJacobs/sentivent", "sentivent_unified_document", split="test") # Runnable empty baseline; replace this list with model predictions. predictions = [ { "id": row["id"], "tokens": row["tokens"], "annotations": [], "relations": {"event_sentiment": [], "event_coreference": []}, } for row in test ] metric = evaluate.load("GillesJacobs/sentivent_metric") result = metric.compute(predictions=predictions, references=list(test)) summary = { "sentivent_score": result["sentivent_score"], "sentivent_macro_score": result["sentivent_macro_score"], "sentivent_relaxed_score": result["sentivent_relaxed_score"], "sentivent_relaxed_macro_score": result["sentivent_relaxed_macro_score"], } print(summary) # { # "sentivent_score": 0.0, # "sentivent_macro_score": 0.0, # "sentivent_relaxed_score": 0.0, # "sentivent_relaxed_macro_score": 0.0 # } ``` Score version `0.4.0` defaults to `sentivent_score`, the strict mean of five extraction component F1 values weighted by fixed train+validation support. By default, the scorer also returns the unweighted macro companion and both relaxed companions. Set `include_factuality=True` to expand each enabled profile to twelve components by adding seven aligned factuality/context macro-F1 values. Publication computes and freezes the component supports from the exact staged train+validation dataset; evaluated slices only renormalize those fixed supports over active components. This intentionally lets less-attested subtasks weigh less, while the macro companion preserves the equal-component view. See the [metric Space](https://huggingface.co/spaces/GillesJacobs/sentivent_metric), [normative specification](https://github.com/exverbai/sentivent-dataset/blob/main/integrations/huggingface-evaluate-metric/sentivent-metric-specification.md), and [replication appendix](https://github.com/exverbai/sentivent-dataset/blob/main/docs/sentivent_metric/scoring_policy.md) for exact formulas, alternatives, and primary-source literature rationale. Load common model-family variants directly when you need framework-specific rows rather than the canonical document target: ```python from datasets import load_dataset dataset_id = "GillesJacobs/sentivent" sentence_rows = load_dataset(dataset_id, "sentivent_unified_sentence") # Sentence-window structured IE continuous_docs = load_dataset(dataset_id, "sentivent_unified_document_continuous") # Continuous-span extractors bio_token_rows = load_dataset(dataset_id, "sentivent_unified_bio_sentence") # BIO token classifiers textee_event_rows = load_dataset(dataset_id, "ere_textee_sentence_e2e") # TextEE event extraction oneie_joint_rows = load_dataset(dataset_id, "sentivent_unified_oneie_sentence") # OneIE-style event-sentiment rows uie_spot_asoc_rows = load_dataset(dataset_id, "sentivent_unified_uie_sentence") # UIE / Spot-Asoc generation absa_quad_rows = load_dataset(dataset_id, "iabsa_quad_sentence") # ABSA quad extraction ``` ## What SENTiVENT Provides SENTiVENT is intended for reproducible evaluation of information extraction and implicit sentiment in financial news. - Expert annotations for economic events, event arguments, sentiment expressions, sentiment targets, and event-implied sentiment. - A joint document-level target that links what happened, who or what it involved, and whether the news implies positive or negative sentiment. - Stable train/dev/test splits matching the SENTiVENT papers for benchmark comparison. - Ready-to-load document, sentence, token-label, ABSA, TextEE, UIE, OneIE, OmniEvent, DyGIE++, and LLM-evaluation views. Implicit sentiment captures cases where factual business-news content changes a reader-investor's attitude toward a company, asset, person, product, or event because readers know what is desirable or undesirable in financial markets, even without words like `great` or `bad`. Implicit sentiment in financial news is challenging because articles are written objectively and the evidence is lexically diverse and requires common-sense world knowledge to resolve. ## Uses SENTiVENT is intended for researchers and ML practitioners evaluating financial NLP systems, information extraction models, sentiment models, and LLM agents on human-annotated financial news. The corpus covers English company-specific business-news articles about 30 S&P 500 companies from June 2016-May 2017, so results may not transfer directly to other languages, later market periods, informal text, non-US companies, or financial-advice settings. The anonymized raw WebAnno/UIMA XMI source export is included under `raw_webanno_export/` for users who need the original annotation files. It is a source artifact, not a recommended modeling or evaluation config. ## Main Task - The primary public config is `sentivent_unified_document`, the compact view for joint economic event extraction, implicit economic sentiment, and event-targeted sentiment in one structured target. - `sentivent_unified_document` uses the full article text as input and emits event triggers, participants, fillers, sentiment expressions, non-event targets, and event-sentiment links with token, character, and text spans. - `sentivent_unified_sentence` mirrors the same schema one source sentence at a time for smaller models, prompt-size constrained evaluation, and sentence-window pipelines; cross-sentence arguments, targets, and relation endpoints are omitted. - Use `sentivent_unified_document_continuous` only for tools that cannot consume discontinuous spans; it fills span gaps and is therefore a derived lossy evaluation view. ## Joint Document Row Shape The default config is an article-level JSON object. `text` is the model input; `annotations` and `relations` are the structured target. This compact example shows the main nested shapes; long token and annotation lists are shortened. ```json { "id": "doc-1:sentivent_unified_document", "split": "train", "text": "Acme raised its 2017 revenue forecast after strong demand.", "tokens": ["Acme", "raised", "its", "2017", "revenue", "forecast", "..."], "annotations": [ { "id": "ev-aal00-01", "kind": "event", "text": "raised", "span": { "discontinuous": false, "tokens": [1], "chars": [[5, 11]], "text": ["raised"] }, "event_type": "Revenue", "event_subtype": "Increase_Revenue", "modality": false, "negated": false, "realis": "actual", "polarity": "positive", "scoped_polarity": "positive", "participants": [ { "id": "part-aal00-01", "argument_id": "arg-aal00-01", "kind": "participant", "role": "Company", "text": "Acme", "span": { "discontinuous": false, "tokens": [0], "chars": [[0, 4]], "text": ["Acme"] } } ], "fillers": [ { "id": "fill-aal00-01", "kind": "filler", "role": "TIME", "text": "2017", "span": { "discontinuous": false, "tokens": [3], "chars": [[16, 20]], "text": ["2017"] } } ] }, { "id": "se-aal00-01", "kind": "sentiment", "text": "strong", "span": { "discontinuous": false, "tokens": [7], "chars": [[44, 50]], "text": ["strong"] }, "polarity": "positive", "scoped_polarity": "positive", "negated": false, "uncertain": false, "targets": [ { "id": "stgt-aal00-01", "sentiment_target_id": "stgt-link-aal00-01", "kind": "sentiment_target", "target_kind": "participant", "text": "Acme", "span": { "discontinuous": false, "tokens": [0], "chars": [[0, 4]], "text": ["Acme"] } } ] } ], "relations": { "event_sentiment": [ { "id": "es-aal00-01", "from": "se-aal00-01", "to": "ev-aal00-01", "polarity": "positive" } ], "event_coreference": [] } } ``` ## Corpus And Annotations SENTiVENT contains company-specific English financial news about 30 S&P 500 companies from June 2016-May 2017. Articles were selected from diverse online business-news sources for sector balance, temporal spread, topical diversity, source quality, and suitability for detailed linguistic annotation; duplicate, low-relevance, templated, and non-company-specific items were removed. The annotations cover ACE/ERE-style economic events and implicit economic sentiment in one joint layer: event triggers, event types/subtypes, participants, fillers, event coreference, event attributes, sentiment expressions, entity/aspect/event targets, polarity, and event-implied sentiment. The holdout test set is a gold-standard expert-adjudicated reference set from a three-annotator agreement study. Reported agreement is almost-perfect for event main type (Fleiss kappa=0.877, Krippendorff alpha=0.874), substantial for event full type/subtype (kappa=0.813, alpha=0.809), and substantial for directly annotated sentiment polarity (kappa=0.778, alpha=0.782). | Item | Total | Train/Dev | Test | | --- | ---: | --- | ---: | | documents | 288 | 228 train / 30 dev | 30 | | sentences | 6,883 | 5,475 train / 681 dev | 727 | | tokens | 170,398 | 135,317 train / 17,955 dev | 17,126 | | event mentions | 6,245 | 4,636 train / 625 dev | 984 | | event arguments | 13,780 | 10,135 train / 1,346 dev | 2,299 | | participant arguments | 10,581 | 7,751 train / 1,048 dev | 1,782 | | filler arguments | 3,199 | 2,384 train / 298 dev | 517 | | event coreference links | 1,364 | 1,021 train / 125 dev | 218 | | event type labels | 18 | - | - | | event subtype labels | 42 | - | - | | sentiment expressions | 3,669 | 2,954 train / 387 dev | 328 | | sentiment-target tuples | 4,429 | 3,531 train / 483 dev | 415 | | event-sentiment links | 102 | 71 train / 11 dev | 20 | ## Recommended Configs | Config | Best for | Task and references | | --- | --- | --- | | sentivent_unified_document | Canonical joint benchmark for ML and LLM evaluation | Full-article structured IE: extract economic event triggers, types/subtypes, participants, fillers, implicit sentiment expressions/targets, and event-sentiment links. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4), [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554), and [SENTiVENT thesis](https://biblio.ugent.be/publication/8728891). | | sentivent_unified_document_continuous | Systems that cannot represent discontinuous spans | Same joint document task for span-limited extractive tools; discontinuous spans are filled to one covering extent, so this is a lossy convenience view. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4) and [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | | sentivent_unified_sentence | Smaller-context joint sentence evaluation | One row per source sentence with the same event/sentiment schema as the document target. Cross-sentence arguments, targets, and relation endpoints are cut so each row is self-contained. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4), [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554), and [SENTiVENT thesis](https://biblio.ugent.be/publication/8728891). | | sentivent_unified_sentence_continuous | Sentence-window tools that cannot represent discontinuous spans | Same sentence-level joint task, after row-local filtering, with retained discontinuous spans filled to one covering extent. This is a lossy convenience view for span-limited tools. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4) and [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | ## Dataset Configs - `sentivent_unified_document` is the default and recommended config for the full joint extraction task over article text. - `sentivent_unified_document_continuous` is a derived evaluation variant that fills discontinuous spans to continuous ranges. - `sentivent_unified_sentence` is the smaller-context sentence-window version of the same joint task; cross-sentence endpoints are cut so every row is self-contained. - `sentivent_unified_sentence_continuous` applies the same sentence-window filtering and fills retained discontinuous spans to continuous ranges. - `sentivent_unified_document_with_canonical_referents` is an opt-in variant that keeps document-level canonical-reference evidence inline. - `sentivent_unified_sentence_with_canonical_referents` is the row-local canonical-reference audit variant for sentence windows. - Interoperability configs expose complete task views for common event extraction, implicit ABSA, UIE, OneIE, OmniEvent, DyGIE++, TextEE, and BIO pipelines. | Config | Description | train / validation / test | | --- | --- | --- | | sentivent_unified_document | Primary joint document-level benchmark: extract economic events, participants, fillers, implicit sentiment expressions, entity/aspect/event targets, polarity, and event-sentiment links. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4), [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554), and [SENTiVENT thesis](https://biblio.ugent.be/publication/8728891). | 228 / 30 / 30 | | sentivent_unified_document_continuous | Lossy continuous-span variant of the joint document extraction task for tools that cannot represent discontinuous spans. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4) and [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | 228 / 30 / 30 | | sentivent_unified_document_with_canonical_referents | Opt-in joint document config with the same resolved spans as the default view plus inline `resolved_from` evidence for canonical referents, including standalone canonical-reference annotation items when needed. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4), [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554), and [SENTiVENT thesis](https://biblio.ugent.be/publication/8728891). | 228 / 30 / 30 | | sentivent_unified_sentence | Sentence-window version of the joint event and implicit-sentiment task: one row per source sentence, with cross-sentence arguments, targets, and relation endpoints omitted so each row is self-contained. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4), [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554), and [SENTiVENT thesis](https://biblio.ugent.be/publication/8728891). | 5475 / 681 / 727 | | sentivent_unified_sentence_continuous | Lossy continuous-span variant of the sentence-window joint task. It keeps the same sentence filtering, then fills retained discontinuous spans without crossing sentence boundaries. See the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4) and [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | 5475 / 681 / 727 | | sentivent_unified_sentence_with_canonical_referents | Sentence-window audit config with inline `resolved_from` evidence only when both the weak mention and canonical referent are inside the same sentence row. Cross-sentence referents are not imported. See the [SENTiVENT thesis](https://biblio.ugent.be/publication/8728891). | 5475 / 681 / 727 | | sentivent_unified_bio_sentence | Joint event and sentiment sequence labeling: one BIO tag per token, with `ner_tags` for Hugging Face token-classification code and sidecars for overlaps. See [HF token classification](https://huggingface.co/tasks/token-classification), the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4), and the [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | 5475 / 681 / 727 | | ere_textee_sentence_e2e | TextEE-style sentence/window rows for event detection plus argument extraction; sentence configs follow the processed JSONL keys used by TextEE. See [TextEE](https://github.com/ej0cl6/TextEE/blob/main/data/README.md) and the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4). | 5475 / 681 / 727 | | ere_textee_sentence_eae | TextEE-style sentence/window rows for event argument extraction; sentence configs follow the processed JSONL keys used by TextEE. See [TextEE](https://github.com/ej0cl6/TextEE/blob/main/data/README.md) and the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4). | 5475 / 681 / 727 | | ere_textee_sentence_ed | TextEE-style sentence/window rows for event trigger detection/classification; sentence configs follow the processed JSONL keys used by TextEE. See [TextEE](https://github.com/ej0cl6/TextEE/blob/main/data/README.md) and the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4). | 5475 / 681 / 727 | | iabsa_asqp_sentence | ASQP-style aspect sentiment quad prediction as delimiter-line rows with character spans and 0/1/2 polarity ids. See [ASQP](https://github.com/Datastory-CN/ASQP-Datasets) and the [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | 5475 / 681 / 727 | | iabsa_aste_sentence | ASTE-style aspect sentiment triplet extraction as delimiter-line rows and companion `.txt` split files. See [ASTE](https://github.com/xuuuluuu/SemEval-Triplet-data) and the [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | 5475 / 681 / 727 | | iabsa_quad_sentence | Implicit economic ABSA quad extraction: target, aspect category, opinion expression, and polarity. See [ASQP](https://github.com/Datastory-CN/ASQP-Datasets) and the [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | 5475 / 681 / 727 | | ere_dygiepp_document | Upstream DyGIE++ document-level event extraction rows with document-token indices, NER spans, and event argument records. See [DyGIE++](https://github.com/dwadden/dygiepp/blob/master/doc/data.md) and the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4). | 228 / 30 / 30 | | sentivent_unified_uie_sentence | UIE text-to-spot-asoc sentence rows for joint event and sentiment extraction as UIE spot-association targets. See [UIE](https://github.com/universal-ie/UIE/blob/main/docs/DATASETS.md), the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4), and the [implicit sentiment paper](https://www.mdpi.com/2079-9292/10/20/2554). | 5475 / 681 / 727 | | ere_omnievent_sentence | OmniEvent unified event/entity rows; intended for legacy parity or framework adapters. See [OmniEvent](https://github.com/THU-KEG/OmniEvent/tree/main/scripts/data_processing) and the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4). | 5474 / 681 / 727 | | sentivent_unified_oneie_sentence | OneIE/TextEE-style event rows with SENTiVENT sentiment-target relation extensions; intended for legacy parity or framework adapters. See [OneIE](https://aclanthology.org/2020.acl-main.713/), [TextEE](https://github.com/ej0cl6/TextEE/blob/main/data/README.md), and the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4). | 5474 / 681 / 727 | | ere_oneie_sentence | OneIE/TextEE-style event rows; intended for legacy parity or framework adapters. See [OneIE](https://aclanthology.org/2020.acl-main.713/), [TextEE](https://github.com/ej0cl6/TextEE/blob/main/data/README.md), and the [event extraction paper](https://doi.org/10.1007/s10579-021-09562-4). | 5474 / 681 / 727 | ## Splits The public configs preserve the original SENTiVENT v1 paper split membership `v1`: 228 train documents, 30 dev documents, and 30 test documents. Hugging Face publishes the source `dev` split as `validation`; document membership is unchanged. ## Citation and License If you use SENTiVENT, please cite the dataset itself: ```bibtex @dataset{jacobs2020sentivent, title={SENTiVENT Bench: Economic Events and Implicit Sentiment in Financial News}, author={Jacobs, Gilles}, publisher={OSF}, year={2020}, version={1.1}, doi={10.17605/OSF.IO/8JEC2}, url={https://doi.org/10.17605/OSF.IO/8JEC2} } ``` For event extraction or annotation methodology, also cite: ```bibtex @article{jacobs2022sentiventenablingeconomicevents, title={SENTiVENT: enabling supervised information extraction of company-specific events in economic and financial news}, author={Jacobs, Gilles and Hoste, Veronique}, journal={Language Resources and Evaluation}, volume={56}, number={1}, pages={225--257}, year={2022}, doi={10.1007/s10579-021-09562-4}, url={https://doi.org/10.1007/s10579-021-09562-4} } ``` When using the sentiment annotations or sentiment configurations, also cite: ```bibtex @article{jacobs2021finegrainedimplicitsentiment, title={Fine-Grained Implicit Sentiment in Financial News: Uncovering Hidden Bulls and Bears}, author={Jacobs, Gilles and Hoste, Veronique}, journal={Electronics}, volume={10}, number={20}, pages={2554}, year={2021}, doi={10.3390/electronics10202554}, url={https://doi.org/10.3390/electronics10202554} } ``` For annotation guidelines and more details: ```bibtex @phdthesis{jacobs2021dissertation, title={Extracting Fine-Grained Events and Sentiment from Economic News}, author={Jacobs, Gilles}, school={Ghent University}, year={2021}, url={https://biblio.ugent.be/publication/8728891} } ``` The SENTiVENT dataset, annotations, schemas, release metadata, dataset card, and documentation are licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0); see [LICENSE](./LICENSE). Executable integration adapters and configuration under [`integrations/`](./integrations/) are licensed under the [Apache License 2.0](./LICENSES/LICENSE-APACHE-2.0.txt). See the [license scope notice](./LICENSES/NOTICE.md) for the complete file-level boundary and third-party exclusions. The citation requests above are not additional license conditions. ## Release Provenance - Release tag: `sentivent-guidelines-v1.1-20260714-g7b759a38329c`. - Annotation guidelines version: `v1.1`. - Build timestamp: `2026-07-14T23:04:09.400815+00:00`. - Builder git commit: `7b759a38329c`. - Machine-readable release tag metadata is in `metadata/release_tags.json`.