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TextBlob | speech tagging , noun phrase extraction | Natural Language Processing library

 by   sloria Python Version: 0.7.0 License: MIT

 by   sloria Python Version: 0.7.0 License: MIT

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kandi X-RAY | TextBlob Summary

TextBlob is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. TextBlob has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install TextBlob' or download it from GitHub, PyPI.
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • TextBlob has a medium active ecosystem.
  • It has 7825 star(s) with 1030 fork(s). There are 273 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 82 open issues and 151 have been closed. On average issues are closed in 178 days. There are 13 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of TextBlob is 0.7.0
TextBlob Support
Best in #Natural Language Processing
Average in #Natural Language Processing
TextBlob Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • TextBlob has 0 bugs and 0 code smells.
TextBlob Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
TextBlob Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • TextBlob has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • TextBlob code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
TextBlob Security
Best in #Natural Language Processing
Average in #Natural Language Processing
TextBlob Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • TextBlob is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
TextBlob License
Best in #Natural Language Processing
Average in #Natural Language Processing
TextBlob License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • TextBlob releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • TextBlob saves you 47698 person hours of effort in developing the same functionality from scratch.
  • It has 55759 lines of code, 5370 functions and 286 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
TextBlob Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
TextBlob Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

kandi has reviewed TextBlob and discovered the below as its top functions. This is intended to give you an instant insight into TextBlob implemented functionality, and help decide if they suit your requirements.

  • Parse a string
    • Find prepositions in tokens
    • Count the number of words in the list
    • Find the chunks in the tag - string
  • Extract words from a sentence
    • Tokenize a sentence
    • Normalize a chunk of tags
    • Train the model
  • Analyze the text
    • Parse a sentence
      • Find the version number
        • Detect language
          • Return the format of the file
            • Train a classification
              • Return a copy of the word
                • Analyze text
                  • A dictionary of word counts
                    • Convert to JSON
                      • Return the next row
                        • Validate parameter
                          • Apply the grammar
                            • Translates source to given language
                              • Update the classifier
                                • Tokenize text
                                  • Return a list of POS tags in the text blob
                                    • Extract noun phrases from text

                                      Get all kandi verified functions for this library.

                                      Get all kandi verified functions for this library.

                                      TextBlob Key Features

                                      Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

                                      how to pass user defined string in tweet cursor search

                                      copy iconCopydownload iconDownload
                                      search = f'#{a} -filter:retweets lang:en
                                      
                                      search = f'from:{a} -filter:retweets lang:en
                                      
                                      search = f'#{a} -filter:retweets lang:en
                                      
                                      search = f'from:{a} -filter:retweets lang:en
                                      

                                      Telegram bot html parsemode is giving string instead of parsing it

                                      copy iconCopydownload iconDownload
                                      url= botsUrl+ "/sendMessage?chat_id={}&text={}".format(chat_id,msg,parse_mode = ParseMode.HTML)
                                      

                                      sentiment analysis of a dataframe

                                      copy iconCopydownload iconDownload
                                      dataset = dataset.explode("adjectives")
                                      

                                      sentiment analysis of a dataframe using if else statements

                                      copy iconCopydownload iconDownload
                                      df['sentiment'] = np.select(
                                          [
                                              dataset['polarity'] > 0,
                                              dataset['polarity'] == 0
                                          ],
                                          [
                                              "Positive",
                                              "Neutral"
                                          ],
                                          default="Negative"
                                      )
                                      
                                      df['sentiment'] = np.select([dataset['polarity'] > 0, dataset['polarity'] == 0], ["Positive", "Neutral"], "Negative")
                                      
                                      df['sentiment'] = np.select(
                                          [
                                              dataset['polarity'] > 0,
                                              dataset['polarity'] == 0
                                          ],
                                          [
                                              "Positive",
                                              "Neutral"
                                          ],
                                          default="Negative"
                                      )
                                      
                                      df['sentiment'] = np.select([dataset['polarity'] > 0, dataset['polarity'] == 0], ["Positive", "Neutral"], "Negative")
                                      
                                      print('Positive:')
                                      print(dataset.loc[dataset['polarity'] > 0, ['adjectives', 'polarity']])
                                      print('Neutral:')
                                      print(dataset.loc[dataset['polarity'] == 0, ['adjectives', 'polarity']])
                                      print('Negative:')
                                      print(dataset.loc[dataset['polarity'] < 0, ['adjectives', 'polarity']])
                                      

                                      Why my output return in a strip-format and cannot be lemmatized/stemmed in Python?

                                      copy iconCopydownload iconDownload
                                      import pandas as pd
                                      import nltk
                                      from textblob import TextBlob
                                      import functools
                                      import operator
                                      
                                      df = pd.DataFrame({'text': ["spellling", "was", "working cooking listening","studying"]})
                                      
                                      #tokenization
                                      w_tokenizer = nltk.tokenize.WhitespaceTokenizer()
                                      def tokenize(text):
                                          return [w for w in w_tokenizer.tokenize(text)]
                                      df["text2"] = df["text"].apply(tokenize)
                                      
                                      
                                      # spelling correction
                                      def spell_eng(text):
                                          text = [TextBlob(str(w)).correct() for w in text] #CHANGE
                                          #convert from tuple to str
                                          text = [functools.reduce(operator.add, (w)) for w in text] #CHANGE
                                          return text
                                      
                                      df['text3'] = df['text2'].apply(spell_eng)
                                      
                                      
                                      # lemmatization/stemming
                                      def stem_eng(text):
                                          lemmatizer = nltk.stem.WordNetLemmatizer()
                                          return [lemmatizer.lemmatize(w,'v') for w in text] 
                                      df['text4'] = df['text3'].apply(stem_eng)
                                      df['text4']
                                      

                                      OSError: E053 Could not read config.cfg Spacy on colab

                                      copy iconCopydownload iconDownload
                                      !pip install spacytextblob
                                      !python -m textblob.download_corpora
                                      !python -m spacy download en_core_web_sm
                                      

                                      &quot;HTTPError: HTTP Error 404: Not Found&quot; while using translation function in TextBlob

                                      copy iconCopydownload iconDownload
                                      url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                          # url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                          url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                          # url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                          url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                          # url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                          url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                      C:\Users\behai\anaconda3\Lib\site-packages\textblob\translate.py
                                      
                                          # url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                          url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      C:\Users\behai\anaconda3\pkgs\textblob-0.15.3-py_0\site-packages\textblob\translate.py
                                      
                                      # requirements/txt
                                      textblob @ git+https://github.com/sloria/TextBlob@0.17.1#=textBlob
                                      

                                      How to determine 'did' or 'did not' on something

                                      copy iconCopydownload iconDownload
                                      !pip3 install flair
                                      
                                      import flair
                                      flair_sentiment = flair.models.TextClassifier.load('en-sentiment')
                                      
                                      sentence1 = 'the movie received critical acclaim'
                                      sentence2 = 'the movie did not attain critical acclaim'
                                      
                                      s1 = flair.data.Sentence(sentence1)
                                      flair_sentiment.predict(s1)
                                      s1_sentiment = s1.labels
                                      print(s1_sentiment)
                                      
                                      s2 = flair.data.Sentence(sentence2)
                                      flair_sentiment.predict(s2)
                                      s2_sentiment = s2.labels
                                      print(s2_sentiment)
                                      
                                      print(s1_sentiment)
                                      [POSITIVE (0.9995)]
                                      
                                      print(s2_sentiment)
                                      [NEGATIVE (0.9985)]
                                      
                                      !pip3 install flair
                                      
                                      import flair
                                      flair_sentiment = flair.models.TextClassifier.load('en-sentiment')
                                      
                                      sentence1 = 'the movie received critical acclaim'
                                      sentence2 = 'the movie did not attain critical acclaim'
                                      
                                      s1 = flair.data.Sentence(sentence1)
                                      flair_sentiment.predict(s1)
                                      s1_sentiment = s1.labels
                                      print(s1_sentiment)
                                      
                                      s2 = flair.data.Sentence(sentence2)
                                      flair_sentiment.predict(s2)
                                      s2_sentiment = s2.labels
                                      print(s2_sentiment)
                                      
                                      print(s1_sentiment)
                                      [POSITIVE (0.9995)]
                                      
                                      print(s2_sentiment)
                                      [NEGATIVE (0.9985)]
                                      
                                      !pip3 install flair
                                      
                                      import flair
                                      flair_sentiment = flair.models.TextClassifier.load('en-sentiment')
                                      
                                      sentence1 = 'the movie received critical acclaim'
                                      sentence2 = 'the movie did not attain critical acclaim'
                                      
                                      s1 = flair.data.Sentence(sentence1)
                                      flair_sentiment.predict(s1)
                                      s1_sentiment = s1.labels
                                      print(s1_sentiment)
                                      
                                      s2 = flair.data.Sentence(sentence2)
                                      flair_sentiment.predict(s2)
                                      s2_sentiment = s2.labels
                                      print(s2_sentiment)
                                      
                                      print(s1_sentiment)
                                      [POSITIVE (0.9995)]
                                      
                                      print(s2_sentiment)
                                      [NEGATIVE (0.9985)]
                                      

                                      Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas

                                      copy iconCopydownload iconDownload
                                      # https://www.kaggle.com/new-york-state/nys-nyc-transit-subway-entrance-and-exit-data
                                      import kaggle.cli
                                      import sys, requests, urllib
                                      import pandas as pd
                                      from pathlib import Path
                                      from zipfile import ZipFile
                                      
                                      # fmt: off
                                      # download data set
                                      url = "https://www.kaggle.com/new-york-state/nys-nyc-transit-subway-entrance-and-exit-data"
                                      sys.argv = [sys.argv[0]] + f"datasets download {urllib.parse.urlparse(url).path[1:]}".split(" ")
                                      kaggle.cli.main()
                                      zfile = ZipFile(f'{urllib.parse.urlparse(url).path.split("/")[-1]}.zip')
                                      dfs = {f.filename: pd.read_csv(zfile.open(f)) for f in zfile.infolist() if Path(f.filename).suffix in [".csv"]}
                                      # fmt: on
                                      
                                      df_subway = dfs['nyc-transit-subway-entrance-and-exit-data.csv']
                                      
                                      from shapely.geometry import Point, MultiPoint
                                      from shapely.ops import nearest_points
                                      import geopandas as gpd
                                      
                                      geometry = [Point(xy) for xy in zip(df_subway['Station Longitude'], df_subway['Station Latitude'])]
                                      
                                      # Coordinate reference system :
                                      crs = {'init': 'EPSG:4326'}
                                      
                                      # Creating a Geographic data frame 
                                      gdf_subway_entrance_geometry = gpd.GeoDataFrame(df_subway, crs=crs, geometry=geometry).to_crs('EPSG:5234')
                                      gdf_subway_entrance_geometry
                                      
                                      df_yes_entry = gdf_subway_entrance_geometry
                                      df_yes_entry = gdf_subway_entrance_geometry[gdf_subway_entrance_geometry.Entry=='YES']
                                      df_yes_entry
                                      
                                      # randomly select a point....
                                      gpdPoint = gdf_subway_entrance_geometry.sample(1).geometry.tolist()[0]
                                      pts = MultiPoint(df_yes_entry['geometry'].values) # does not work with a geopandas series, works with a numpy array
                                      pt = Point(gpdPoint.x, gpdPoint.y)
                                      #[o.wkt for o in nearest_points(pt, pts)]
                                      for o in nearest_points(pt, pts):
                                        print(o)
                                      

                                      Textblob OCR throws 404 error when trying to translate to another language

                                      copy iconCopydownload iconDownload
                                      url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      url = "http://translate.google.com/translate_a/t?client=webapp&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      
                                      url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
                                      

                                      Community Discussions

                                      Trending Discussions on TextBlob
                                      • how to pass user defined string in tweet cursor search
                                      • Telegram bot html parsemode is giving string instead of parsing it
                                      • sentiment analysis of a dataframe
                                      • sentiment analysis of a dataframe using if else statements
                                      • How to remove unexpected parameter and attribute errors while importing data for sentiment analysis from twitter?
                                      • How to apply a user-defined function to a column in pandas dataframe?
                                      • Why my output return in a strip-format and cannot be lemmatized/stemmed in Python?
                                      • OSError: E053 Could not read config.cfg Spacy on colab
                                      • &quot;HTTPError: HTTP Error 404: Not Found&quot; while using translation function in TextBlob
                                      • How to determine 'did' or 'did not' on something
                                      Trending Discussions on TextBlob

                                      QUESTION

                                      how to pass user defined string in tweet cursor search

                                      Asked 2022-Apr-15 at 19:02

                                      Q)how to pass user defined string in tweet_cursor search i am trying to get tweets as per the user by taking input in a and passing variable a please help

                                      currently it is searching for only a literally instead of variable a defined by user

                                      `import textblob import pandas as pd import matplotlib.pyplot as plt import re

                                      api_key = 'xxxxxxxxxxxx'
                                      api_key_secret = 'xxxxxxxxxxx'
                                      access_token = 'xxxxxxxxxxx'
                                      access_token_secret = 'xxxxxxxxxxxxxxxxxxx'
                                      
                                      authenticator = tweepy.OAuthHandler(api_key, api_key_secret)
                                      authenticator.set_access_token(access_token, access_token_secret)
                                      
                                      api = tweepy.API(authenticator, wait_on_rate_limit=True)
                                      
                                      a=input(print("enter player name"))
                                      
                                      search= f'#(a) -filter:retweets lang:en'
                                      
                                      tweet_cursor = tweepy.Cursor(api.search_tweets, q=search, tweet_mode='extended').items(100)
                                      
                                      tweets = [tweet.full_text for tweet in tweet_cursor]
                                      
                                      tweets_df = pd.DataFrame(tweets, columns=['Tweets'])
                                      
                                      for _, row in tweets_df.iterrows():
                                          row['tweets'] = re.sub('https\S+', '', row['Tweets'])
                                          row['tweets'] = re.sub('#\S+', '', row['Tweets'])
                                          row['tweets'] = re.sub('@\S+', '', row['Tweets'])
                                          row['tweets'] = re.sub('\\n', '', row['Tweets'])
                                      print(tweets_df)
                                      print(tweets)
                                      
                                      
                                      tweets_df['Polarity'] = tweets_df['Tweets'].map(lambda tweet: textblob.TextBlob(tweet).sentiment.polarity)
                                      tweets_df['Result'] = tweets_df['Polarity'].map(lambda pol: '+' if pol > 0 else '-')
                                      
                                      positive = tweets_df[tweets_df.Result == '+'].count()['Tweets']
                                      negative = tweets_df[tweets_df.Result == '-'].count()['Tweets']
                                      
                                      print(positive)
                                      print(negative)
                                      langs = ['Positive', 'Negative']
                                      students = [positive,negative]
                                      a=plt.bar(langs,students)
                                      a[0].set_color('g')
                                      a[1].set_color('r')
                                      
                                      plt.xlabel("Tweet Sentiment")
                                      plt.ylabel("No. of Tweets")
                                      plt.title("Sentiment Analysis")
                                      plt.legend
                                      

                                      plt.show() `

                                      ANSWER

                                      Answered 2022-Apr-15 at 19:02

                                      In Python f-Strings, you have to use curly braces around the variable.

                                      search = f'#{a} -filter:retweets lang:en
                                      

                                      But this will search for retweets containing the hashtag a.

                                      If you want to search from the tweets of the user a, you should use:

                                      search = f'from:{a} -filter:retweets lang:en
                                      

                                      Please see the Twitter API documentation for all the search operators.

                                      Source https://stackoverflow.com/questions/71883919

                                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                                      Vulnerabilities

                                      No vulnerabilities reported

                                      Install TextBlob

                                      You can install using 'pip install TextBlob' or download it from GitHub, PyPI.
                                      You can use TextBlob like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

                                      Support

                                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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