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Shopify/ E-commerce specialist, web designer 

Hourly - Est. Budget - $18.00, Expiry - Mar 29, 2021, Proposals(1) - posted at 7 hours ago
I need a Shopify E-commerce specialist.Tasks:-Inventory input-label/barcode creation-website improvement.-instagram e-commerce-development of website Shopify

Need website designer to work colaboratively and launch asap 

Fixed - Est. Budget - $50.00, Expiry - Mar 29, 2021, Proposals(9) - posted at 7 hours ago
We are looking for a creative, motivated individual who'd like to work with a unique team to design and launch webiste asap. Located in Freeport and need quality product asap.

update php website 

Fixed - Est. Budget - $1,000.00, Expiry - Mar 29, 2021, Proposals(13) - posted at 8 hours ago
update working ecomerce website code from php 5.2 to php 7.4

WE need a new homepage for our company. Modern and simple one ... 

Hourly - Est. Budget - $24.00, Expiry - Mar 29, 2021, Proposals(20) - posted at 8 hours ago
I am looking for an expert-level freelancer with Web Design experience who can help me plan and complete my project.

WordPress Site Update Design 

Fixed - Est. Budget - $500.00, Expiry - Mar 29, 2021, Proposals(16) - posted at 9 hours ago
1. Update WordPress landing page and fix the misalignments of images2. Update About Us page with sleeker design 3. Add business departments to bottom4. If our site is responsible for all devices, that would be idealWe want to improve upon what we have now by doing the following: 1. Fix misalignment in landing page 2. Loading spe...read more

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Create GP account + Publish App "xstreak" 

Fixed - Est. Budget - $65.00, Expiry - Mar 29, 2021, Proposals(8) - posted at 9 hours ago
1. create google play account and pay for developer account2. build apk file3. publish the application

Prestashop developer for data sync 

Fixed - Est. Budget - $100.00, Expiry - Mar 29, 2021, Proposals(7) - posted at 9 hours ago
Hi,I need Prestashop developer to create data sync from Prestashop to POS system and from POS System to Prestashop.Previously i do this with Woocommerce, but since i never use Prestashop i will need help with this.You will focus in Prestashop.- I already have script that regularly make POST request to external resources. You onl...read more

Kartra Developer 

Hourly - Est. Budget - $24.00, Expiry - Mar 29, 2021, Proposals(8) - posted at 10 hours ago
I am a Real Estate Owner, Investor and Realtor. I am looking for a Kartra expert! Similar to Click funnels.I need someone who has experienced with Kartra site development and funnel builder to setup our events sites. I will share video link with shortlisted candidates.You must know how to- Create landing pages- Setup email marke...read more

Wordpress / WooCommerce 

Hourly - Est. Budget - $18.00, Expiry - Mar 29, 2021, Proposals(19) - posted at 10 hours ago
Hello,I am an Industrial Designer who is in need of some help with an e-commerce website. I am designing the UX/UI in adobe XD but I need someone who is a wizard in wordpress to set the site up. I am familiar with WordPress and I now that there are restrictions on how the design will play out depending on themes etc. Having that...read more

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Web developer needed for creating a responsive WordPress Theme-based website 

Fixed - Est. Budget - $500.00, Expiry - Mar 29, 2021, Proposals(10) - posted at 10 hours ago
I am looking for someone who can help setting up my website.I have a very clear idea about my website and how it should look like. I have a Wordpress theme (Divin Pro) which I am aiming to use. But I open to suggestions. I want to change a few things and add some plugins and would need someone to modify/customise a theme. I have...read more

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NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient manner. By utilizing NLP and its parts, one can organize the massive chunks of text information, perform various automated tasks and solve a wide range of issues like – automatic summarization, machine translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation etc.

 

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to lexical resources like WordNet, along with a collection of text processing libraries for classification, tokenization, stemming, and tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

 

NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

 

Downloading and installing NLTK

  1. Install NLTK: run pip install nltk
  2. Test installation: run python then type import nltk and run nltk.download() and download all packages.

 

Pre-Processing with NLTK

The main issue with text data is that it's all in text format. However, the Machine learning algorithms need some variety of numerical feature vector so as to perform the task. Thus before we have a tendency to begin with any NLP project we'd like to pre-process it to form it ideal for working. Basic text pre-processing includes:

 

  • Converting the whole text into uppercase or lowercase, in order that the algorithm doesn't treat the same words completely different in several cases.
  • Tokenization: Process of converting the normal text strings into a list of tokens i.e. words that we actually want. The NLTK data package includes a pre-trained Punkt tokenizer for English.

 

           import nltk

           from nltk.tokenize import word_tokenize

           text = "God is Great! I won a lottery."

           print(word_tokenize(text))

           Output: ['God', 'is', 'Great', '!', 'I', 'won', 'a', 'lottery', '.']

 

  • Noise removal: Process of removing everything that isn’t in a standard number or letter.
  • Stop word removal: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”). We would not want these words or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to be stop words. NLTK (Natural Language Toolkit) in python has a list of stopwords stored in sixteen different languages. You can find them in the nltk_data directory.  home/Saad/nltk_data/corpora/stopwords is the directory address.

           import nltk

           from nltk.corpus import stopwords

           set(stopwords.words('english'))

 

  • Stemming: Stemming is the process of reducing the words to its root form. Example if we were to stem the following words: “Connects”, “Connecting”, “Connected”, “and Connection”, the result would be a single word “Connect”.

           # import these modules

           from nltk.stem import PorterStemmer

           from nltk.tokenize import word_tokenize   

           ps = PorterStemmer()  

           # choose some words to be stemmed

           words = ["Connect", "Connects", “Connected”, "Connecting", "Connection", "Connections"]

 

           for w in words:

           print(w, " : ", ps.stem(w)) 

 

  • Lemmatization: Lemmatization is the process of grouping along the various inflected forms of a word in order that they may be analyzed as a single item. Lemmatization is similar to stemming but it brings context to the words. Therefore it links words with similar meaning to one word.

           # import these modules

           from nltk.stem import WordNetLemmatizer  

           lemmatizer = WordNetLemmatizer()  

           print("rocks :", lemmatizer.lemmatize("rocks"))

           print("corpora :", lemmatizer.lemmatize("corpora"))  

           # a denotes adjective in "pos"

          print("better :", lemmatizer.lemmatize("better", pos ="a"))

 

          -> rocks : rock

          -> corpora : corpus

          -> better : good

 

Now we need to transform text into a meaningful vector array. This vector array is a representation of text that describes the occurrence of words within a document. For example, if our dictionary contains the words {Learning, is, the, not, great}, and we want to vectorize the text “Learning is great”, we would have the following vector: (1, 1, 0, 0, 1). A problem is that extremely frequent words begin to dominate within the document (e.g. larger score), however might not contain as much informational content. Also, it will offer additional weight to longer documents than shorter documents.

 

One approach is to rescale the frequency of words or the scores for frequent words called Term Frequency-Inverse Document Frequency.

 

  • Term Frequency: is a scoring of the frequency of the word in the current document.

           TF = (Number of times term t appears in a document)/ (Number of terms in the document)

 

  • Inverse Document Frequency: It is a scoring of how rare the word is across documents.

           IDF = 1+log(N/n), where, N is the number of documents and n is the number of documents a term t has appeared in.

 

           Tf-idf weight is a weight often used in information retrieval and text mining.

           Tf-IDF can be implemented in scikit learn as:

 

           from sklearn.feature_extraction.text import TfidfVectorizer

           corpus = [

           ...     'This is the first document.’

           ...     'This document is the second document.’

           ...     'And this is the third one.’

           ...     'Is this the first document?',]

           >>> vectorizer = TfidfVectorizer()

           >>> X = vectorizer.fit_transform(corpus)

           >>> print(vectorizer.get_feature_names())

           ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']

           >>> print(X.shape)

           (4, 9)

 

  • Cosine similarity: TF-IDF is a transformation applied to texts to get two real-valued vectors in vector space. We can then obtain the Cosine similarity of any pair of vectors by taking their dot product and dividing that by the product of their norms. That yields the cosine of the angle between the vectors. Cosine similarity is a measure of similarity between two non-zero vectors.

           Cosine Similarity (d1, d2) =  Dot product(d1, d2) / ||d1|| * ||d2||

 

          import numpy as np

          from sklearn.metrics.pairwise import cosine_similarity

          # vectors

          a = np.array([1,2,3])

          b = np.array([1,1,4])

          # manually compute cosine similarity

          dot = np.dot(a, b)

          norma = np.linalg.norm(a)

          normb = np.linalg.norm(b)

          cos = dot / (norma * normb)

 

After completion of cosine similarity matric we perform algorithmic operation on it for Document similarity calculation, sentiment analysis, topic segmentation etc.

 

I have done my best to make the article simple and interesting for you, hope you found it useful and interesting too.

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