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Tweet Sentiment Analysis
course content

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

bookData Preprocessing

Data preprocessing refers to the techniques used to prepare raw data for further analysis or modeling. The goal of preprocessing is to clean, transform, and format the data so that it can be used effectively in an analysis or model.

Methods description

  • The .dropna() method in Pandas is used to remove rows or columns with missing values (NaN). Setting inplace=True modifies the DataFrame in place, meaning the changes are applied directly to the original DataFrame, and it returns None;
  • The .drop_duplicates() method is used to remove duplicate rows from the DataFrame. Setting inplace=True modifies the DataFrame in place, removing duplicate rows, and it returns None.

Task

  1. Drop NaNs from our dataset.
  2. Drop duplicates from our dataset.

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Data preprocessing refers to the techniques used to prepare raw data for further analysis or modeling. The goal of preprocessing is to clean, transform, and format the data so that it can be used effectively in an analysis or model.

Methods description

  • The .dropna() method in Pandas is used to remove rows or columns with missing values (NaN). Setting inplace=True modifies the DataFrame in place, meaning the changes are applied directly to the original DataFrame, and it returns None;
  • The .drop_duplicates() method is used to remove duplicate rows from the DataFrame. Setting inplace=True modifies the DataFrame in place, removing duplicate rows, and it returns None.

Task

  1. Drop NaNs from our dataset.
  2. Drop duplicates from our dataset.

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 4
AVAILABLE TO ULTIMATE ONLY
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