Text Mining In R | Natural Language Processing | Data Science Certification Training | Edureka

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In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session:
(01:18) Need for Text Mining
(03:56) What Is Text Mining?
(05:42) What is NLP?
(07:00) Applications of NLP
(08:33) Terminologies in NLP
(14:09) Demo
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#textmining #textminingwithr #naturallanguageprocessing #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial
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About the Course
Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyze Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
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Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies.
For online Data Science training, please write back to us at [e-mail] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

[@edurekaIN]

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Data Science Training Certification Curriculum, Visit our Website: bit.ly/37q65Oc

[@MarcelloNesca]

WOW! thank you i am trying to learn NLP through R for my thesis, and this was a brilliant explanation thank you!!

[@Continentalky]

where does the value 30 come from in the write:ines(as.character(docs[[30]])) come from?

[@RPenahli]

This is incredible! so much information explained in a very simple way. Thank you!

[@sanilsarang8643]

Wonderful tutorial! Thank you so much !

[@gonzalosilva3661]

Very interesting and clear explanation!

[@Mekinhumbel]

Great concise introduction to a fascinating topic--thanks for this! Gladly liked and subbed!

[@Shahrvand23]

Excellent video and great explanation!

[@goodbook6865]

That is a great video, thank you, but I wish that you showed how the data looks like after each function 9e.g., remove punctuations). However, great work. Thank you!

[@edurekaIN]

We are very glad to hear that your a learning well with our contents :) continue to learn with us and dont forget to subscribe our channel so that you dont miss any updates !

[@thej1091]

Amazing! Miss! You killed it! thank You very much!

[@edurekaIN]

Thanks for the compliment, Kiran! We are glad you loved the video.

[@mhmodsaid1022]

Best explanation ever,, thank you!

[@mdmeherulislam7796]

thank you

[@duckdong22]

This video is really helpful!!! Thank you so much! I wish you nothing but joy, health, and love!!

[@anmfaisal964]

Thank you for a great session. Best explained

[@edurekaIN]

Thanks for appreciating our work, Faisal!

[@adnan2911]

really good !

[@maxmax-cm7eh]

great video . can i have the csv or file that you used. thank you

[@edurekaIN]

Glad you liked it ! We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss any video from our channel

[@piyushtripathi5008]

Very nice 👍

[@market_forecasting]

Awesome video, thanks. Could you please share the code?

[@edurekaIN]

Hi, kindly drop in your email id to help us assist you with the required source codes. Cheers :)

[@dafni5674]

25:20 23:46

[@tammammohamed9374]

where can I find the documents ?

[@edurekaIN]

Hi Tammam, kindly drop in your email id to help us assist you with the required documents for your referencing. Happy learning :)

[@karthikravinatha736]

i am getting this error Error in Corpus(DirSource("F:/textmining")) : could not find function "Corpus" inspect(docs) Error in inspect(docs) : could not find function "inspect" i have installed packages nlp and tm please resolve thank you

[@edurekaIN]

Hi Karthik, the way to solve this problem is to detach a library before using inspect() method for an object made with other library. detach(package:tm, unload=TRUE) then load the required libraries and use inspect() method. Hope this is helpful.

[@priyankashukla8206]

and how to analyze Reviews given in . CSV file?

[@edurekaIN]

Hey Priyanka, the read.csv command reads a CSV (Comma Separated Value) file from disk. Such files represent a table whose rows are represented by single lines in the files and columns are marked by a separator character within lines. Arguments of the command can be set to specify whether the CSV file contains a line with column names (header = TRUE or FALSE) and the character set.

[@priyankashukla8206]

what extension of file in TextMining folder?

[@edurekaIN]

Hi Priyanka, extension to be mined can be loaded into R from different source formats. It can come from text files(.txt),pdfs (.pdf),csv files(.csv) etc., but no matter what the source format is, to be used in the TextMining package it is turned into a “corpus”. Hope this is helpful!

[@revathirasamsetty2024]

Short and very detailed explanation!

[@natashasamuel9346]

Great class. Keep up the good work. Thank You, Natasha Samuel

[@edurekaIN]

Thank you so much : ) We are glad to be a part of your learning journey. Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )

[@EustaquioSantimano]

Thank you for the quick and fast introduction.

[@archanabulbul]

Simple, precise, hands-on and clear. 👍

[@edurekaIN]

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[@Indiasgotlatent2]

thank you so much edureka for bringing such video! I love it.

[@edurekaIN]

Thanks for appreciating our efforts!

[@nileshnath541]

Brilliant!

[@edurekaIN]

Thank you so much for your review on our channel  Great to hear that Edureka is helping you learn better . We’ll strive to make even better learning contents/courses in the future ! Do subscribe the channel for more updates : )

[@josephinepasipanodya2750]

Is text mining similar to thematic analysis

[@edurekaIN]

Hi Josephine, Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. Thematic analysis is one of the most common forms of analysis within qualitative research. It emphasizes pinpointing, examining, and recording patterns within data.