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)
attributes:
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.
attributes:
analyzer.entities - dictionary of entities and their tags (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()
attributes:
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.
optional parameters:
entity_type - string specifying the entity type to analyze. Default value is None. Default action is the analyze all entity types.
Metadata
Release files for sentimizer 2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sentimizer-2.0.tar.gz | 3.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sentimizer-2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.1 kB
Release files / sentimizer-2.0.tar.gz
| Download URL | sentimizer-2.0.tar.gz |
|---|---|
| Size | 3.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a357c8bf4e71b973264b7a3c950a0bd4c86797dd7be0729cbaf58c988a3ddfae
|
|
BLAKE2b-256 checksum How to use checksums |
dad9bf94a98fe4c3bc9315f0d84c48853aa8bfec3191ef3ad7ba134a70cfd44d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.12
|
Release files / sentimizer-2.0-py3-none-any.whl
| Download URL | sentimizer-2.0-py3-none-any.whl |
|---|---|
| Size | 4.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3118a131fab4a268b97f726526bf32d9368232f58260958d17dc6eace1db3883
|
|
BLAKE2b-256 checksum How to use checksums |
664673f86e0ed94efcd14a2ec904d75ca0b10745b76a7b6d93bb59041f26dbf2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.12
|