tabpy.models.scripts.SentimentAnalysis.SentimentAnalysis() - Code Metrics - tableau/TabPy - Measure and Improve Code Quality continuously with Scrutinizer

SentimentAnalysis() B
last analyzed 2024-11-25 22:00 UTC

↳ Parent: tabpy.models.scripts.SentimentAnalysis

Complexity

Conditions

Size

Total Lines	28
Code Lines	19

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	19
dl	0
loc	28
rs	8
c	0
b	0
f	0
cc	7
nop	2

from textblob import TextBlob
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from tabpy.models.utils import setup_utils


import ssl

_ctx = ssl._create_unverified_context
ssl._create_default_https_context = _ctx


nltk.download("vader_lexicon")
nltk.download("punkt")


def SentimentAnalysis(_arg1, library="nltk"):
    """
    Sentiment Analysis is a procedure that assigns a score from -1 to 1
    for a piece of text with -1 being negative and 1 being positive. For
    more information on the function and how to use it please refer to
    tabpy-tools.md
    """
    if not (isinstance(_arg1[0], str)):
        raise TypeError

    supportedLibraries = {"nltk", "textblob"}

    library = library.lower()
    if library not in supportedLibraries:
        raise ValueError

    scores = []
    if library == "nltk":
        sid = SentimentIntensityAnalyzer()
        for text in _arg1:
            sentimentResults = sid.polarity_scores(text)
            score = sentimentResults["compound"]
            scores.append(score)
    elif library == "textblob":
        for text in _arg1:
            currScore = TextBlob(text)
            scores.append(currScore.sentiment.polarity)
    return scores


if __name__ == "__main__":
    setup_utils.deploy_model(
        "Sentiment Analysis",
        SentimentAnalysis,
        "Returns a sentiment score between -1 and 1 for " "a given string",
    )


1			from textblob import TextBlob
2			import nltk
3			from nltk.sentiment.vader import SentimentIntensityAnalyzer
4			from tabpy.models.utils import setup_utils
5
6
7			import ssl
8
9			_ctx = ssl._create_unverified_context
10			ssl._create_default_https_context = _ctx
11
12
13			nltk.download("vader_lexicon")
14			nltk.download("punkt")
15
16
17			def SentimentAnalysis(_arg1, library="nltk"):
18			"""
19			Sentiment Analysis is a procedure that assigns a score from -1 to 1
20			for a piece of text with -1 being negative and 1 being positive. For
21			more information on the function and how to use it please refer to
22			tabpy-tools.md
23			"""
24			if not (isinstance(_arg1[0], str)):
25			raise TypeError
26
27			supportedLibraries = {"nltk", "textblob"}
28
29			library = library.lower()
30			if library not in supportedLibraries:
31			raise ValueError
32
33			scores = []
34			if library == "nltk":
35			sid = SentimentIntensityAnalyzer()
36			for text in _arg1:
37			sentimentResults = sid.polarity_scores(text)
38			score = sentimentResults["compound"]
39			scores.append(score)
40			elif library == "textblob":
41			for text in _arg1:
42			currScore = TextBlob(text)
43			scores.append(currScore.sentiment.polarity)
44			return scores
45
46
47			if __name__ == "__main__":
48			setup_utils.deploy_model(
49			"Sentiment Analysis",
50			SentimentAnalysis,
51			"Returns a sentiment score between -1 and 1 for " "a given string",
52			)
53

tableau / TabPy

SentimentAnalysis() B last analyzed 2024-11-25 22:00 UTC

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SentimentAnalysis() B
last analyzed 2024-11-25 22:00 UTC