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Tweet Sentiment Analysis
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Course Content

Tweet Sentiment Analysis

Tweet Sentiment Analysis

IntroductionReading the DataPrint out the Basic Info of our DatasetData PreprocessingEDAPlot the DistributionText CleaningWords CountGenerate a Word CloudText Sentiment AnalysisSentiment Classification

bookWords Count

Now we would like to explore the most represented number in our DataFrame. For this reason we will create a collection where we will store the most frequent words and then, plot it.

Methods description

  • from collections import Counter; import nltk: Imports the Counter class from the collections module and the nltk library;
  • from nltk.corpus import stopwords: Imports a list of common stopwords from NLTK;
  • nltk.download("stopwords"): Downloads the stopwords dataset from NLTK;
  • def remove_stopword(x): This defines a function named remove_stopword that takes a list x as input and returns a new list with stopwords removed;
  • return [y for y in x if y not in stopwords.words("english")]: This comprehension expression filters out stopwords from the input list x using the list of English stopwords from NLTK;
  • Counter: A class from the collections module used to count occurrences of elements in a list or iterable;
  • stopwords.words("english"): A method from NLTK that returns a list of stopwords for the English language;
  • temp.most_common(25): Returns the 25 most common elements (words) and their counts from the Counter object temp;
  • temp.iloc[1:,:]: Indexes a DataFrame temp to exclude the first row and select all columns;
  • temp.style.background_gradient(...): Applies a background gradient style to a DataFrame temp.

Task

Create a collection to count word occurrences using the Counter module:

  1. Remove stopwords from our tweets texts.
  2. Create a collection.
  3. Create a DataFrame with the newly created list.
  4. Change the background color to "Blues".

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Now we would like to explore the most represented number in our DataFrame. For this reason we will create a collection where we will store the most frequent words and then, plot it.

Methods description

  • from collections import Counter; import nltk: Imports the Counter class from the collections module and the nltk library;
  • from nltk.corpus import stopwords: Imports a list of common stopwords from NLTK;
  • nltk.download("stopwords"): Downloads the stopwords dataset from NLTK;
  • def remove_stopword(x): This defines a function named remove_stopword that takes a list x as input and returns a new list with stopwords removed;
  • return [y for y in x if y not in stopwords.words("english")]: This comprehension expression filters out stopwords from the input list x using the list of English stopwords from NLTK;
  • Counter: A class from the collections module used to count occurrences of elements in a list or iterable;
  • stopwords.words("english"): A method from NLTK that returns a list of stopwords for the English language;
  • temp.most_common(25): Returns the 25 most common elements (words) and their counts from the Counter object temp;
  • temp.iloc[1:,:]: Indexes a DataFrame temp to exclude the first row and select all columns;
  • temp.style.background_gradient(...): Applies a background gradient style to a DataFrame temp.

Task

Create a collection to count word occurrences using the Counter module:

  1. Remove stopwords from our tweets texts.
  2. Create a collection.
  3. Create a DataFrame with the newly created list.
  4. Change the background color to "Blues".

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 8
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