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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.

Are you planing to hire a CSS developer—how can you find a top developer for your front-end or PSD to CSS project?

CSS has been in use for more than 20 years and has become an integral part of any front-end development. Therefore, there is no shortage of developers with CSS listed on their resumes. Locating CSS developers is fairly easy, but choosing the perfect one is that much more of a challenge. This article offers a sampling of effective questions to help you identify the best CSS developers who are experts in their field.

What is CSS?

CSS stands for Cascading Style Sheets, CSS is a programming language that describes the style of a HTML document. If you want to customize your website background image, text colors or border then you need CSS design. Alongside HTML (responsible for structure) and JavaScript (responsible for interactivity), CSS (responsible for style) is one of the big three core components of the web.

Next, we learn about what a CSS developer does, and provide you with a general framework for writing a CSS project description to help you find the right developer for your requirements. How to hire a top css developer to get work done.

What kind of work a CSS developer should deliver to you? A front-end developer uses a combination of HTML, CSS, and JavaScript to build everything a user sees and interacts with on a website—everything from front-end features like fonts and sliders, to the overall manner in which web content like photos, videos, and articles are displayed in your web browser. A CSS developer who specializes in CSS, taking .psd files and mockups and writing the CSS code that incorporates all of the colors, padding, margins, and more that comprise those designs. Beyond the fundamentals, they can work magic with raw CSS, are well versed in preprocessors like LESS/Sass, and may even use a front-end CSS framework like Bootstrap or Foundation.

Hire the best CSS Developers Work with the world's best talent on Toogit — the top growing freelancing website trusted by over 150,000 users.

Writing A CSS Development Project Description

After you get a firm idea of your project deliverables, it’s time to write a project description. The way you write a description will determine the quality of developer that you’ll attract. It’s important to be concise yet detailed enough so developers interested in your project can submit proposals with fairly accurate cost and time estimates. Here our recommendation to use Toogit’s auto-proposal to speed up your hiring procedure and feel the power of AI in freelancing.

The title of your project description can include the type of development that you need. You know that you need a CSS developer, but why specifically a front-end developer specialized in CSS? The title should attract CSS developers with the specific technologies or skills you require for your project.

Next is the project overview. Describe what you’re planning to build or what you’ll need the developer to do. Be as detailed as possible, and include any wireframes or mockups that can help you attract the talented developer for your needs. 

Part of your description should also define the deliverables including any designs, documentation, or source code. 

Sample CSS Project Description

Below sample will help you to write a perfect project description. 

Project Title:

CSS Developer for a Fashion design website 

Description: 

We’re looking for an expert CSS developer to help us build an exciting new fashion design website template. The project is based on the (MongoDB, AngularJS, and Node.js) stack, so familiarity using Bootstrap with AngularJS is required.

The right developer will be able to provide us with the following skills and services:

• Translation of designer mock-ups and wireframes into front-end code
• Front-end integration with a MEAN back-end
• Unit testing
• Bootstrap, LESS, AngularJS
• Familiarity with API Creation and RESTful services

Project Scope & Deliverables:

While much of the project has already been completed, we still need additional support to help us polish our website and meet our launch deadline in 4 months (mm/dd/yyyy). We will need the following three deliverables:

Deliverable #1 by (date) 

Deliverable #2 by (date) 

Deliverable #3 by (date)

Hire a CSS Designer

On Toogit.com you can hire CSS coders and designers to make your web design and custom CSS project shine. Get started today.

Conclusion

For a top CSS developer, read our css interview question and answer section this might come off as a bit basic. However, It cover most of the core CSS concepts and principles, and provide a starting point for evaluating individuals. Being able to discuss CSS principles and concepts in a clear and coherent manner will demonstrate candidate’s communication skills as well as their theoretical and peripheral subject knowledge. Finding true CSS expert is a challenge. We hope you find the interview questions to be a useful foundation in your quest for the elite few among CSS developers. Finding such candidates is well worth the effort, as they will undoubtedly have a significant positive impact on your team’s productivity and results.