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Tweet Sentiment Analysis
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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

Generate a Word Cloud

A word cloud (also known as a tag cloud) is a visual representation of the most frequently used words in a piece of text. The size of each word in the cloud corresponds to its frequency of use, with the most frequently used words appearing larger and more prominent than less frequently used words.

Word clouds are often used to quickly and easily identify the most important or relevant words in a piece of text, such as a document, a webpage, or a set of social media posts. They can be used in a variety of applications, such as text mining, content analysis, and social media monitoring.

Tarefa

  1. Import WordCloud, STOPWORDS, ImageColorGenerator from wordcloud.
  2. Select only the "neutral" tweets (the "sentiment" column).
  3. Create a plot using the plot_wordcloud() function.

Tarefa

  1. Import WordCloud, STOPWORDS, ImageColorGenerator from wordcloud.
  2. Select only the "neutral" tweets (the "sentiment" column).
  3. Create a plot using the plot_wordcloud() function.

Mark tasks as Completed
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Tudo estava claro?

A word cloud (also known as a tag cloud) is a visual representation of the most frequently used words in a piece of text. The size of each word in the cloud corresponds to its frequency of use, with the most frequently used words appearing larger and more prominent than less frequently used words.

Word clouds are often used to quickly and easily identify the most important or relevant words in a piece of text, such as a document, a webpage, or a set of social media posts. They can be used in a variety of applications, such as text mining, content analysis, and social media monitoring.

Tarefa

  1. Import WordCloud, STOPWORDS, ImageColorGenerator from wordcloud.
  2. Select only the "neutral" tweets (the "sentiment" column).
  3. Create a plot using the plot_wordcloud() function.

Mark tasks as Completed
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Seção 1. Capítulo 9
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