Employee Survey Analysis (ESA) Scripts

Employee Survey Analysis ( ESA ) ScriptsYet Another inwrought Language Processing ApplicationAbstraction With this form-up of our, we defy curiously worked on unrivaled of the application of Data Analysis. We have proposed a impudently order for happening come to the fore valuable randomness disclose of the roll up of natural learnings utilizing Python and NLTK libraries. We have refined the utterances of the assorted Employees of a Comp both in the signifier of Raw Data. to all(prenominal) one Remark follows varied stairss such(prenominal) as Cleansing which removes all told the errors in the celebrates made by the exploiter, Taging which tags word harmonizing to the diverse types of verbs or adjectives employ in the remarks, Lumping which includes choosing a perticualr phrase pop out of the cleansed remark by usage of a appropriate grammar regulation, Category Generation which includes variant types of tier generated for the words which sewer be use for b ring foreing antithetical layers user remarks. This includes the usage of Python as a tool where NLTk is added as a Natural Language Processor which is used for contrary sorts of linguistic communications. You whitethorn happen the elaborate distinguish closely our methodological analysiss in the later(a) parts of this paper.Keywords Python, NLTK, Tokenizer 8 , Lemmatizer 9 , Stemmer 9 , Chunker, Tagger.I. IntroductionWith the growing of IT sphere of influence over the past few old ages, informations handling and its analysis had constrain really hard. M all(prenominal) companies trades with a big sum of informations and they have purchased diametrical tools from different companies manage IBM, Microsoft, etc for informations storage and its analysis. Data Analysis fundamentally provides us the method to pull out some valuable information out of some natural facts. It contains some(prenominal) Fieldss which be essential to be undertaken such as taking all the erro rs, change overing it into that signifier which our tool hatful translate, saying regulations for it usage, happening the results and take supportive actions on the footing of these results. The land of Data Analytics is pity huge and have legion(predicate) another(prenominal) attacks related to informations blood and mold and in this paper we testament be discoursing on the one of the of aftermath application of Data Analytics.Let us give apprehend what Data Analysis is with illustration of a unmarried named Lee who had a wont of composing dairy. He started observing each and both happening of his life get spileing from his birth boulder clay now. With the crystallize of clip, he have written a batch of information about himself which reflects different phases of his life. Suppose if another man-to-man goes through each and every incident of Lee s life and analysis what he used to wish when he was below 10 old ages of age or which portion of his life was unforgetta ble. This analysis of the natural information and happen out the valuable information out of it is categorized with the term Data Analytics. I think now we ar in a place to understand the relevant nomenclatures used in this paper. So I would wish to yield the existent methodological analysis of our research paper.II. A Brief METHODOLOGYThis paper demonstrates a novel method which help user to pull out utile information from the clump of a natural information. It includes a method/ codes which include the usage of touch on of categories and maps which help in pull military expedition a utile information out of input informations. There are m each utile maps which help in pull outing information that are included here. Some of them may be named as, Tokenizer, Taggers, Chunkers, Stemmers, Transformation of Chunkers and Taggers and many more. These methods or categories work on the tool Named as Python 2.7.6 which is postulate to be steered and wakeless configured in the syste m. Every Code that is executed require to be merchandiseed through assorted bundles present in the library. In this undertaking, we have processed the informations and produced the different classs out of it and through that we have extracted what user really meant to state in his/her remarks. You may happen the elaborate account as what this paper is all approximately in ulterior portion.A.PythonPython 1 is considered as a high layer linguistic communication, a degree in front of C and C++ . It is fundamentally developed for growing applications or books for transforming different signifiers of linguistic communications like English, French, German and many more. Python have a alone characteristic which differentiate it from other linguistic communications like C, C++ or Java is that it uses white limitless indenture instead than curly brackets. Presently, the latest version of python in the commercialise is Python 3.4.1 was released on May 18th, 2014. But we have used Pyt hon 2.7.6.B.NLTKNLTK 3 is draw as Natural Language Tool Kit. It comprises of library files in different linguistic communications that Python may hold for informations analysis. One is required to import the NLTK bundle in the Python Shell so that its library files back be used by the coder. NLTK includes several(prenominal) characteristics like graphical presentation of informations. some(prenominal) books have been published on the alien belongingss and installations of NLTK which clearly explains things off to any coder who is either novice with python or NLTK or merely an expert. NLTK finds several applications in research work when it comes to Natural Language Processing. It helps in treating school text in several linguistic communications which itself is a large corroborative for modern research workers.III. IMPLEMENTATION OF EMPLOYEE SURVEY ANALYSIS ( ESA ) SCRIPTSA.What s the requirement of ESA Scripts.In Today s universe of Globalization and competitions, It is th e tendency which is followed by every confederacy to form a Engagement and Exit Survey for its employee indoors the organisation to happen out the grounds wherefore people wants to capitulation in or go away their company. When any individual leaves any company, he/she is required to make integral an online study that comprises of assorted Fieldss which cogency be the grounds for his go forthing the Organization. In that study, the inquiries might be in assorted signifiers like Check Boxes, move List, text field, etc. It is pity easy to enter and analysis those inquiries which involve replying through Checkboxes or Scroll List but state of affairs becomes really feverish for the individual who is analysing that informations if the reply is recorded through Text fields or Text split. When speaking about manually reading, the individual, who is reading that informations, will be required to travel through each and every employees remarks to happen what were the grounds why th ey have left the occupation. Each company comprises of 1000s of employees and it is really common in industries that people moves from one organisation to another organisation. So, maintaining the path of all those employees by merely manual reading is a tough undertaking.Figure 1 A Screen Shot of Employee Exit Survey 1 Each company spends a batch of money and resources on their employees on their dressing and growing and hence, wants to happen the grounds why their best employees are go forthing them. Therefore, we are in an pressing demand of something which corporation assist us happening the grounds why any individual is go forthing his/her organisation. Although, thither are several tools in the market by some erratic companies like IBM. But the major point is they all are paid and therefore, require a batch of money to invested to buy them. In examine with these paid tools, these Python Script are unfastened beginning and are extra of cost. Any organisation can besides do alterations in the books harmonizing to its demand. Hence, it is provision us the best ground why to choose for ESA Scripts.B.Functionality of ESA ScriptsESA Scripts performs following actions as specified below It corrects all the Spelling Mistakes.It corrects all the Repeated Words.It performs Lemmatization, Stemming and Tokenization of Data.It performs Antonyms and equivalent word Operations on words.It find out what sort of Verb, Noun or procedural is used by the Employee.It generates Phrases depending upon the type of Grammar Rule one select.Removal of Stop Words.En cryptography and decoding of Special Stop Words.Removal of ASCII Codes.There are many more of import trading operations which comes under these above specified operations which are discussed subsequently as their functions comes.C.Following Big MeasureFirst of all, Remarks of different employees are taken in a individual Column of a CSV file and read line wise. Each Remark comprises of different paragraph hold ing different Spelling errors, repeated characters in a word and many more errors which are required to be upstage earlier we can happen out what individual meant in his/her grounds for go forthing his/her occupation.all(a) the files are required to be stored by.py computer address and all the of import methods or categories are required to be specify in a individual library file so that when utilizing those maps and classes we can import them in a spell and utilize them to make whatsoever we like to make. These methods/classes are defined in library file named as CustomClassLibrary.py and this file is required to be executed at the top sooner utilizing any of the map or category so that these categories work whence whenever they are called in the chief book.There is yet another of import thing that we are required to take attention of. You must either topographic point all you scripts in the legitimate on the capriole directory or you must supply the way where you have plac ed your books. It is passing required and if we do non supply the way of our books decently so it will be traveling to demo mistakes which will return an mistake that current file do non be in our directory.Figure-2Block Diagram Representing Various Processes to be followedThis Purpose has been divided into 3 Classs which are as followsa. Cleansing.b. Tagging and Chunking 12 .c. Category Generation.The above described description can be better explained by the figure given below.A.Cleaning Cleansing, as its name suggests includes the methods which help in cleaning the information which the user has provided. It includes those methods or maps by which one can tokenize informations, correct the spellings, take all the perennial words like if any user wrote love as llooovvvee in a really ardent manner. So they are required to be corrected. There are several Abbreviations that people wrote which are required to be changed to their normal word signifier. so there are several stop wo rds in the sentences which do non lend much to the significance of that sentence are hence required to be remote from that sentence. The process of this is explained as below.First of wholly, we break Paragraph into Sentences and in that process some of the words are changed into ASCII Codes which created job when we save run the social occasion on them and are required to be outside through strip_unicode bid. After taking ASCII Codes we tokenize Sentences into words.Now, explicating each class in full stop below.Figure-3Measure wise Explanation for Above ProcessThese words are processed and all the perennial words like looovvee are changed to love by utilizing repetition replacer map. After that all the short signifiers or the Abbreviations are changed to their full signifiers. All the spelling errors are required to be corrected before continuing farther. This map is imported utilizing import bid and all the methods are required to be defined in our library file named as Cust omClassLibrary.pyAfter rectifying all of our spelling errors, we lemmatize our word if they are found to be of Noun, adjective or Verb. For any other class of words, it traveling to go through the word as it is. After that all the punctuations are removed such as Commas, Exclamation grade, Full Stops, etc.Here, now we are required to code some of the limited words so that they can be used in approaching procedure. We will be coding some of the words and them taking stop words from that list of words. All those word which do non assist in analysing the sentences like can, could, might, etc are removed from the list of words. Once, Stop words removed, we once more decrypt those particular words once more so that they can be processed now. At this measure, we have got the list of words which are traveling to be passed to make Antonym of words which appears after not word.For Example, lets , not , uglify , our , code is changed to lets , beautify , our , code . Therefore, we are at th at place with our Cleansed Data.A.TAGGING AND Unitization Tagging is a procedure of designation different tickets to the word in conformity with the portion of address tagging. For this, we have used Classifier based POS tagger 5 10 which is rather a good tagger. When calculated, its efficiency comes out to be over 90 % which is rather good. For labeling, we passed the information word wise and happen out to which portion of address class it belongs. Either it is a noun or it is a verb or adjectival like vise.We are making labeling in order to bring forth labelled word from where we can make a grammar regulation so that from them, all the words which comes, forms a meaningful phrase and therefore can be wrote in different file.IV. GRAMMAR RULE 11 AND UnitizationA.Chunk Rule NP & A lt RBDTNN.*VB.* & A gt ? & A lt VB.* & A gt ? & A lt .* & A gt ? & A lt JJ.* & A gt ? & A lt JJ.*NN. ? & A gt + This Chunk Rule can be described as the phrase formed will get do wn with nonobligatory Adverb or Determiner or any sort of Noun or any sort of Verb followed by any sort of optional Verb followed by optional any word followed by any sort of optional Adjective and stoping with as many figure of any sort of Adjective or any sort of Noun.B.Category CoevalsFor Category Generation, we have selected those set of tokenized words which are generated from chunked end product. These words are written respectively in different file and we manually create class for that. bid if salary appears in the file so we have created its class as salary problem likewise if family appears in the word so we generated its class as Personal Issues . Once this file is created so we compare each and every word of the file and if we find that word in our decided words file so we are traveling to bring forth that class for that word.Figure-3Distinct Categories defined for Chunked wordsOnce the class is generated, this class is used to bring forth the consequences for the di fferent remarks made by user. It is here shown in the figure below.Figure-4Classs Generated for different Employees remarksV. APPLICATION OF EMPLOYEE SURVEY ANALYSIS ( ESA ) SCRIPTSWe can make sentimental analysis utilizing this application.Sentimental Analysis 7 This is a procedure of analysing the sentiments of a individual, be it positive, negative or assorted emotions.We can utilize the same application for other spheres as good like employment of an employee with the organisation.VI. DecisionThis Paper provides a advanced thought which helps in cut pop the human attempts as individual who is analysing the information of assorted employees who had left every bit now, is non required to travel through each and every employees remarks. Therefore, by running these books we will be able to bring forth what an employee is speaking about, what are the assorted causes which he found in the company which forced him to vacate. Hence, the value of this merchandise goes up when you th ink analysing the information of different users of different states following different linguistic communications.VII. 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