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Vaibhav Digital Marketer Executive, India
$9 /hr
3 Years Exp.
I am a certified google analytics marketer, with pro experience of 3 + years in content creation (text /ads). I really am looking forward to work at t...Read More
Vihan D.Quality Analyst|Automation|Agile Methodology, India
$10 /hr
15 Years Exp.
I am software quality expert with an edge of technology and functional knowledge. I have experience across the industry where I have spent 15 years in...Read More
Chinkey Senior Software QA Engineer, India
$20 /hr
7 Years Exp.
Hi, I am a B. Tech graduate in Information Technology and working as Senior QA Quality Assurance Engineer. I am into software testing field for 7+ y...Read More
Osuobiem G.Web Developer/Designer, Nigeria
$4 /hr
2 Years Exp.
I am a professional website developer/designer, I am very proficient PHP, Laravel framework, Node.js, MySQL, HTML, Vanilla Javascript, and CSS. I have...Read More
Emad Z.System analyst / architect, Kuwait
$30 /hr
0 Years Exp.
System / business analyst Software architect Application support Devloper ITIL certified, Certified manager, PMP
My interest level lies in creating crystal clear documents in word,excel or powerpoint. My past experience helps to have a keen eye on detailing and s...Read More
Divya K.Business analyst, testing, India
$9 /hr
5 Years Exp.
I have 5+ years of experience in Manual testing, user acceptance testing, application development and business analysis
Yashwanth R.Risk & Predictive Business Analytics, India
$45 /hr
15 Years Exp.
A Diligent, forward-thinking professional with 15 years of experience in Business Intelligence (BI) / Data Analytics. Competencies: Risk Analytics,...Read More
Mohit K.Senior Business Analyst/Product Manager , India
$25 /hr
8 Years Exp.
Product manager with 7+ years of experience in Banking domain, RPA and business process management. Hands on experience in automating various banking...Read More
Venkatesh K.Senior Python Developer, India
$50 /hr
4 Years Exp.
I am self learnt Python Developer with 4 years of experience currently working in Standard Chartered GBS as a Senior Developer. Additionally I have Ex...Read More
Vishal Jindal Sr.Business Analyst, India
$20 /hr
6 Years Exp.
Over 5.6 years of rich end-to-end Business Analysis experience in IT/Payments Bank/Legal/Sports/Travel industries. Interacting with International stak...Read More
Nikita Business Analyst , India
$4 /hr
4 Years Exp.
 Pre Sales – Understanding the client’s requirement, their business model by interacting with client on calls and by making documents and presentatio...Read More
Moiz Business Analyst / Consultant, India
$30 /hr
11 Years Exp.
I am a business analyst with more than 11 years experience in IT industry. I am also a consultant and provide strategic consultation to clients throug...Read More
Ritika M.Lead Business Analyst, India
$85 /hr
8 Years Exp.
AI BOT Product Owner and Content Strategist Experience – • Delivered the BOT Features from requirements definition through product launch • Respons...Read More
Prashant C.Business analyst, India
$16 /hr
3 Years Exp.
Working as business analyst
Shivam M.Associate Consultant, India
$6 /hr
2 Years Exp.
I am certified Salesforce Administrator with 2 years of experience in Salesforce platform. I have worked on Salesforce Sales Cloud, Service Cloud, NGO...Read More
Sravani Market Research Analyst, India
$17 /hr
1 Years Exp.
I am an expert in market research analysis with industry experience of 1.5 years. I can perform market research for any domains but with specific dom...Read More
Mujeeb B.Consultant Business Analysis , India
$22 /hr
5 Years Exp.
I am Consultant Business Analyst with 5 years of experience. Experience in mobile and web development in Agile methodology. 2 years of client facing e...Read More
Kushal S.Artificial Intelligence Engineer, India
$100 /hr
5 Years Exp.
I am Currently working on Satellite Image processing for use in Agriculture field and ocean monitoring system. I have worked on Fraud analytics for...Read More
Venkata Business Analyst, India
$7 /hr
7 Years Exp.
I Holds 7 years experience in Business Analysis, Requirement gathering, Design , Documentation and Functional Testing.
Ankita D.Software Tester, India
$10 /hr
4 Years Exp.
QA Engineer by profession with 4+ years of experience in Software Testing. I’m hardworking, confident, self-motivating, responsible and energetic team...Read More
I’ve been a Product Owner/ Business Analyst – 10 years of experience in problem solving with focus on Agile implementations, requirement elicitation,...Read More
Rajesh Deep Business Analyst, India
$1 /hr
8 Years Exp.
Business Analyst with 8 years of experience in different IT Domains such as Telecom, Retail and Legal. I've extensive experience in requirement g...Read More
*Expert in regression / classification / clustering /Neural Nets / Convolutional Neural Nets/ Machine Learing in Trading / doing very well in...Read More
Samreen Research Analyst, India
$25 /hr
3 Years Exp.
I'm into business research (specific to Print and Online media) for 2.5 years. Also, i do business development.
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Articles Related To Sentiment Analysis

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."


           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



  • 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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