Sentimizer will measure sentiment around specific entities within text.
Project description
Sentimizer
About
Sentimizer will measure sentiment around specific entities within text. It is built on NLTK, Spacy, and NRCLex. Output is a dictionary that can be analyzed further, graphed, formulated into a wordcloud, etc.
References
Revision History
- 2022-10-17: initial commit.
Example Usage
pip install sentimizer
Instantiate SentiMizer Object
from sentimizer import SentiMizer
analyzer = SentiMizer()
Load Text
Loads initial body of text.
analyzer.load_text(text : str)
analyzer.text
- attribute contains loaded text (str).
Append Text
For appending additional text to the initial input.
analyzer.append_text(text : str)
Entity Recognition
For identifying entities within the loaded body of text.
analyzer.find_entities()
optional parameters:
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.
analyzer.entities
- dictionary of entities and their taxs (dict).
analyzer.sentences
- dictionary of entities and concatenated sentences containing each entity (dict). Keys are entities and values are the concatenated sentences mentioning that entity.
Measure emotional content
For measuring sentiment and emotional affect of sentences that mention each entity.
analyzer.emote()
analyzer.sentiments
- Vader composite sentiment scores for each entity (dict). Keys are entities and values are the composite sentiment score for that entity.
analyzer.affect
- NRCLex affect scores for each entity (dict). Keys are entities and the values are affect frequency dictionaries.
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