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Sentiment Analysis of Tweets with tidytext

Build an R pipeline that ingests a dataset of tweets, tokenizes and cleans the text, and scores sentiment using tidytext lexicons, then visualizes sentiment trends over time. Learners practice text mining with tidytext, joining sentiment lexicons, and time-based aggregation.

Estimate
~8h
Steps
5
Completed by
0
Proposed by
codeseed.app

tidytext · dplyr · ggplot2

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Project roadmap

  1. 01

    Load and tokenize the tweet dataset

    ~1.5h

    Load a CSV of tweets with timestamps and tokenize the text into individual words using tidytext.

  2. 02

    Clean tokens and remove stop words

    ~1.5h

    Remove stop words, URLs, and punctuation tokens, keeping only meaningful words.

  3. 03

    Score sentiment with a lexicon

    ~2h

    Join tokens against a sentiment lexicon (like bing or afinn) and compute a per-tweet sentiment score.

  4. 04

    Aggregate sentiment over time

    ~1.5h

    Group sentiment scores by day or hour and compute rolling averages.

  5. 05

    Visualize sentiment trends

    ~1.5h

    Plot sentiment over time with ggplot2, highlighting notable spikes or dips.

Resources

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Get a GitHub repo and start building. Your AI reviewer checks each step as you go.

~8h · 5 steps

Tech stack

tidytextdplyrggplot2