Texo 0.0.4

Sentiment Analysis Multiple language and for all products

Texo

Under construction! Not ready for use yet! Currently experimenting and planning!

Developed by Sourabh Singh from Neuroins(c) 2023

Examples of How To Use (Buggy Alpha Version)

Predicating Sentimental Analysis in English

from Texo.Sentimental.Analysis import Sentitweeten

Analysis = Sentitweeten()
Out = Analysis.Tweet_analy('Analysis this Tweet')
print(out)

Predicating Sentimental Analysis in Hindi

from Texo.Sentimental.Analysis import Sentitweeten

Analysis = Sentitweethn()
Out = Analysis.Tweet_anal('Analysis this Tweet')
print(out)

Find all stop_words from Text

from Texo.Word.texo import tex
x = tex()
er = x.stop_words('This is a sample text with some stop words in it.')
print(er)

Find Summary from Text and number of Points of user Choice

from Texo.Word.texo import tex
x = tex()

text = "In literary theory, a text is any object that can be  whether this object is a work of  an arrangement of buildings on a city block, or styles of clothing. " \
       "It is a coherent set of signs that transmits some kind of informative message." \
       "This set of signs is considered in terms of the informative message's content, rather than in terms of its physical form or the medium in which it is represented.  " \
       "Within the field of literary criticism, also refers to the original information content of a particular piece of writing; that is, the "text" of a work is that primal symbolic arrangement of letters as originally composed, apart from later alterations, deterioration, commentary, translations, paratext, etc." \
       "et eleifend tortor mauris in dui. Vivamus ut pulvinar mauris, eget fermentum metus. " \
       "Cras nec varius ipsum. Sed sed neque vel ante vulputate gravida id a nisl. " \
       "Praesent facilisis imperdiet elit at rhoncus. Morbi ac scelerisque risus."

summary = x.summary(text, num_bullet_points=10)
print(summary)

Unet for Image

from Texo.Unet import UnetM
im = UnetM()
im.Imageprepocess('image_path')

#Display image
im.show()

Tokenize of Text and pos

from Texo.Word.texo import tex

#tokenize text without stop-words
tokenize = tex()
words = tokenize.tokenize('This is going Awesome')
print(words)

#tokenize text with stop-words
tokenize = tex()
words = tokenize.tokenize_stopwords('This is going Awesome')
print(words)

#find POS from tokens and return pos in form of array
tokenize = tex()
tokens = ['Running', 'dogs', 'are', 'happier', 'than', 'walked', 'dogs', 'in', 'parks.']
words = tokenize.pos_tag(tokens)
print(words)

#return output in form of token of array
#Display image
im.show()