{"id":18582905,"url":"https://github.com/DemetersSon83/sentimizer","last_synced_at":"2025-11-01T21:30:26.676Z","repository":{"id":61606379,"uuid":"553083831","full_name":"metalcorebear/sentimizer","owner":"metalcorebear","description":"Sentimizer will measure sentiment around specific entities within text.  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It is built on NLTK, Spacy, and NRCLex.  Output is a dictionary that can be analyzed further, graphed, formulated into a wordcloud, etc.\r\n\r\n## References\r\n* https://github.com/explosion/spaCy\r\n* https://github.com/nltk\r\n* https://github.com/metalcorebear/NRCLex\r\n\r\n## Revision History\r\n* 2022-10-17: initial commit.\r\n\r\n## Example Usage\r\n`pip install sentimizer`\r\n\r\n### Instantiate SentiMizer Object\r\n`from sentimizer import SentiMizer`\u003cbr\u003e\u003cbr\u003e\r\n`analyzer = SentiMizer()`\r\n\r\n### Load Text\r\nLoads initial body of text.\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.load_text(text : str)`\u003cbr\u003e\u003cbr\u003e\r\nattributes:\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.text` - attribute contains loaded text (str).\r\n\r\n### Append Text\r\nFor appending additional text to the initial input.\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.append_text(text : str)`\r\n\r\n### Entity Recognition\r\nFor identifying entities within the loaded body of text.\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.find_entities()`\u003cbr\u003e\u003cbr\u003e\r\noptional parameters:\u003cbr\u003e\u003cbr\u003e\r\n`entity_types_of_interest` - list of entity types for recognition.  Default value is `['ORG', 'PERSON', 'FAC', 'GPE', 'LOC', 'EVENT']`  All possible lables include: `CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART` For a description of each, visit https://spacy.io/models/en. \u003cbr\u003e\u003cbr\u003e\r\nattributes:\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.entities` - dictionary of entities and their tags (dict).\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.sentences` - dictionary of entities and concatenated sentences containing each entity (dict).  Keys are entities and values are the concatenated sentences mentioning that entity.\r\n\r\n### Measure Emotional Content\r\nFor measuring sentiment and emotional affect of sentences that mention each entity.\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.emote()`\u003cbr\u003e\u003cbr\u003e\r\nattributes:\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.sentiments` - Vader composite sentiment scores for each entity (dict).  Keys are entities and values are the composite sentiment score for that entity.\u003cbr\u003e\u003cbr\u003e\r\n`analyzer.affect` - NRCLex affect scores for each entity (dict).  Keys are entities and the values are affect frequency dictionaries.\r\noptional parameters:\u003cbr\u003e\u003cbr\u003e\r\n`entity_type` - string specifying the entity type to analyze. Default value is `None`.  Default action is the analyze all entity types.","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDemetersSon83%2Fsentimizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDemetersSon83%2Fsentimizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDemetersSon83%2Fsentimizer/lists"}