The Resource Machine learning : Twitter sentiment analysis in Python, Loonycorn [editor]

Machine learning : Twitter sentiment analysis in Python, Loonycorn [editor]

Label
Machine learning : Twitter sentiment analysis in Python
Title
Machine learning
Title remainder
Twitter sentiment analysis in Python
Statement of responsibility
Loonycorn [editor]
Title variation
Byte-size chunks : sentiment analysis in Twitter using Python
Contributor
Editor
Publisher
Subject
Language
eng
Summary
"Use Python and the Twitter API to build your own sentiment analyzer. Sentiment analysis, or opinion mining, is a field of neuro-linguistic programming that deals with extracting subjective information, like positive/negative, like/dislike, and emotional reactions. In this Twitter sentiment analysis in Python online course, you'll learn real examples of why sentiment analysis is important and how to approach specific problems using sentiment analysis. Learn why sentiment analysis is useful and how to approach the problem using both rule-based and machine learning-based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set. All this is in the run up to a serious project to perform Twitter sentiment analysis. We'll spend some time on regular expressions which are pretty handy to know as we'll see in our code-along."--Resource description page
Cataloging source
UMI
Characteristic
representational
LC call number
Q325.5
http://bibfra.me/vocab/relation/on-screenpresenter
9HmxoaUgDa0
PerformerNote
Presenter, Janani Ravi
Label
Machine learning : Twitter sentiment analysis in Python, Loonycorn [editor]
Publication
Note
Title and publication information from resource description page (Safari, viewed August 1, 2017)
http://library.link/vocab/branchCode
  • net
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Configuration of playback channels
unknown
Content category
two-dimensional moving image
Content type code
tdi
Content type MARC source
rdacontent
Control code
ocn998838941
Dimensions
unknown
Extent
1 online resource (1 streaming video file (3 hr., 12 min., 51 sec.))
Form of item
online
Media category
  • computer
  • video
Media MARC source
  • rdamedia
  • rdamedia
Media type code
  • c
  • v
Medium for sound
other
Other physical details
digital, sound, color
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000880
http://library.link/vocab/recordID
.b37435024
Sound
sound
Sound on medium or separate
sound on medium
Specific material designation
  • remote
  • other
System control number
  • safari998838941
  • (OCoLC)998838941
Video recording format
other

Library Locations

    • Deakin University Library - Geelong Waurn Ponds CampusBorrow it
      75 Pigdons Road, Waurn Ponds, Victoria, 3216, AU
      -38.195656 144.304955
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