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Machine learning and Lexicon approach to Sentiment analysis

PUBLISHER :- Jaro Algo

LANGUAGE :- English

PRIZE :- 17.10$ 0$

Machine learning and Lexicon approach to Sentiment analysis

WHO CAN ACCESS THIS COURSE :-

  • Beginner Python developers curious about data science
  • Anyone who is interested in data analysis
  • People who wants to include sentiment analysis for their projects

WHAT ARE YOUR REQUIREMENTS FOR THIS COURSE ?

  • Basic Python knowledge (I explain each step so you can understand what I am doing)

WHAT YOU ARE GOING TO LEARN FROM THIS COURSE ?

  • The most effective method to make twitter engineer record and associate with twitter API
  • Download Tweets, clean and store them in to Pandas DataFrame
  • Find out about Tokenization, Lemmatization, Stemming and substantially more
  • Perform Sentiment investigation with Vader and TextBlob vocabularies
  • Find out about Machine learning way to deal with Sentiment Analysis

DESCRIPTION

Figure out how to interface and download tweets through Twitter API. From that point I will tell you the best way to clean this information and set them up for assessment examination. There are two most usually utilized ways to deal with opinion examination so we will take a gander at the two of them. Initial one is Lexicon based methodology where you can utilize arranged dictionaries to investigate information and get slant of given content. Second one is Machine learning approach where we train our own model on named information and afterward we show it new information and ideally our model will give us conclusion. Toward the end you will have the option to construct your own content to dissect estimation of hundreds or even large number of tweets about theme you pick.

COURSE CONTENT

7 sections • 21 lectures • 3h 16m total length

Introduction1 lecture • 4min

  • IntroductionPreview03:38

Getting started1 lecture • 7min

  • Anaconda and Jupyter notebookPreview07:03

Twitter API2 lectures • 9min

  • Account setup for Twitter APIPreview02:49
  • Python access to APIPreview05:48

Data cleaning and visualisation3 lectures • 34min

  • Storing tweets into Dataframe13:30
  • Text cleaning12:26
  • Visualisation08:24

Introduction to NLP2 lectures • 16min

  • What is NLP04:30
  • Introduction to NLTK11:25

Lexicon based approach6 lectures • 39min

  • What is lexicon based approach01:32
  • TextBlob04:32
  • Vader03:50
  • Python test of Vader and TextBlob09:54
  • Lexicon based sentiment analysis 110:39
  • Lexicon based sentiment analysis 208:16

Machine learning approach6 lectures • 1hr 28min

  • Cleaning data for RandomForest model18:58
  • Building RandomForest model20:19
  • Code cleaning12:02
  • Sentiment test10:34
  • Test on labeled data from NLTK14:01
  • Test on labeled data from Kaggle12:12

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