Datasets:

takala
/

financial_phrasebank

- {"sentences_allagree": {"description": "The key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement: all annotators\nagree, >=75% of annotators agree, >=66% of annotators agree and >=50% of\nannotators agree.\n", "citation": "@article{Malo2014GoodDO,\n title={Good debt or bad debt: Detecting semantic orientations in economic texts},\n author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},\n journal={Journal of the Association for Information Science and Technology},\n year={2014},\n volume={65}\n}\n", "homepage": "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["negative", "neutral", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "financial_phrasebank", "config_name": "sentences_allagree", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 303371, "num_examples": 2264, "dataset_name": "financial_phrasebank"}}, "download_checksums": {"https://huggingface.co/datasets/financial_phrasebank/resolve/main/data/FinancialPhraseBank-v1.0.zip": {"num_bytes": 681890, "checksum": "0e1a06c4900fdae46091d031068601e3773ba067c7cecb5b0da1dcba5ce989a6"}}, "download_size": 681890, "post_processing_size": null, "dataset_size": 303371, "size_in_bytes": 985261}, "sentences_75agree": {"description": "The key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement: all annotators\nagree, >=75% of annotators agree, >=66% of annotators agree and >=50% of\nannotators agree.\n", "citation": "@article{Malo2014GoodDO,\n title={Good debt or bad debt: Detecting semantic orientations in economic texts},\n author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},\n journal={Journal of the Association for Information Science and Technology},\n year={2014},\n volume={65}\n}\n", "homepage": "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["negative", "neutral", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "financial_phrasebank", "config_name": "sentences_75agree", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 472703, "num_examples": 3453, "dataset_name": "financial_phrasebank"}}, "download_checksums": {"https://huggingface.co/datasets/financial_phrasebank/resolve/main/data/FinancialPhraseBank-v1.0.zip": {"num_bytes": 681890, "checksum": "0e1a06c4900fdae46091d031068601e3773ba067c7cecb5b0da1dcba5ce989a6"}}, "download_size": 681890, "post_processing_size": null, "dataset_size": 472703, "size_in_bytes": 1154593}, "sentences_66agree": {"description": "The key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement: all annotators\nagree, >=75% of annotators agree, >=66% of annotators agree and >=50% of\nannotators agree.\n", "citation": "@article{Malo2014GoodDO,\n title={Good debt or bad debt: Detecting semantic orientations in economic texts},\n author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},\n journal={Journal of the Association for Information Science and Technology},\n year={2014},\n volume={65}\n}\n", "homepage": "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["negative", "neutral", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "financial_phrasebank", "config_name": "sentences_66agree", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 587152, "num_examples": 4217, "dataset_name": "financial_phrasebank"}}, "download_checksums": {"https://huggingface.co/datasets/financial_phrasebank/resolve/main/data/FinancialPhraseBank-v1.0.zip": {"num_bytes": 681890, "checksum": "0e1a06c4900fdae46091d031068601e3773ba067c7cecb5b0da1dcba5ce989a6"}}, "download_size": 681890, "post_processing_size": null, "dataset_size": 587152, "size_in_bytes": 1269042}, "sentences_50agree": {"description": "The key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement: all annotators\nagree, >=75% of annotators agree, >=66% of annotators agree and >=50% of\nannotators agree.\n", "citation": "@article{Malo2014GoodDO,\n title={Good debt or bad debt: Detecting semantic orientations in economic texts},\n author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},\n journal={Journal of the Association for Information Science and Technology},\n year={2014},\n volume={65}\n}\n", "homepage": "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["negative", "neutral", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "financial_phrasebank", "config_name": "sentences_50agree", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 679240, "num_examples": 4846, "dataset_name": "financial_phrasebank"}}, "download_checksums": {"https://huggingface.co/datasets/financial_phrasebank/resolve/main/data/FinancialPhraseBank-v1.0.zip": {"num_bytes": 681890, "checksum": "0e1a06c4900fdae46091d031068601e3773ba067c7cecb5b0da1dcba5ce989a6"}}, "download_size": 681890, "post_processing_size": null, "dataset_size": 679240, "size_in_bytes": 1361130}}