Text Mining and Natural Language Processing in R

Read CSV and Excel data in R by setting the working directory, using read.csv and read.table with header options, handling semicolon separators, and loading Excel files via read_excel.

Learn to read online csv data into R, handle metadata, skip top rows, set headers, and access data frames before indexing and subsetting.

Learn how to read a zipped folder from a web source in R by downloading to a temp file, unzipping it, accessing a CSV within, and cleaning up temp files.

Learn to read data from a database in R by connecting to a SQLite database, listing tables, reading a CO2 table, filtering rows, and safely disconnecting.

Read json data in r, parse world bank json files, and extract country ids and iso codes using lapply and custom functions to access regional attributes.

Learn to read data from pdf documents with libris pdf tools, extract text from pages, access tables, and clean data to obtain tabular information, noting encryption and variability.

Discover how to read and extract tables from PDF documents in R using stabiliser and tabulator, install via devtools and GitHub, and convert results to data frames for analysis.

Learn to read data from csv, excel, zip, databases, json, and pdf sources, including textual and tabular data, and begin deriving insights from text in R.

Learn to read data from a public Google Sheets in R by installing the Google Sheets package, authenticating with a Google account, and converting sheets to data frames.

Apply text mining and natural language processing in R to read and extract data from online HTML tables, using XML and readHTMLTable on the 2016 Olympics medal table from Wikipedia.

Read html tables from wikipedia with rvest by using an xpath to extract the target table into R; demonstrates with the 2016 olympics medal table and united kingdom heritage sites.

Extract and clean a single HTML table from a web page, rename columns, remove header rows, and separate winners and losers using string extraction to parse scores.

Learn web scraping and text mining in R by reading HTML pages, extracting the main content, and isolating Trump and Clinton speeches to analyze word usage.

Use the Selector Gadget to identify HTML elements such as the product title on Amazon, extract text, clean whitespace, and prepare data for text mining and NLP in R.

Scrape IMDB pages with rvest and the Selecter gadget to extract rankings, titles, runtimes, and genres, build a data frame, and gain insights from 2017 film data.

Discover another way to access and inspect elements on a dynamic New Zealand tourism page, inspecting popular cities via linked HTML pages.

Learn to extract and clean tabular data from html pages using selectors to access web elements, with examples from Wikipedia, Amazon reviews, IMDb, and APIs.

Learn what an API is and how it lets you access data from websites and social media, using Guardian, Facebook, Twitter, and Foursquare APIs to extract information.

Use the Guardian API in R to extract headlines and text on a topic, register for a developer key, and clean the data by removing encodings, links, and HTML tags.

Extract public Facebook textual data for text mining in R using a short-lived access token. Retrieve groups, pages, and posts with like and comment counts for analysis.

Explore Facebook data with the graph API, obtain a page's posts using its page id and token, and prepare unstructured textual data for analysis.

Learn to set up a Twitter app for mining data: create the app, obtain consumer key, consumer secret, access token and secret, configure a callback, and prepare to extract tweets.

Connect to the Twitter API in R using consumer keys and access tokens, read tweets from a hashtag and from Theresa May, and extract the tweet text for analysis.

Learn to extract data from Twitter in R by configuring credentials, connecting to the Twitter API, and retrieving tweets with date ranges and geolocation such as the Barcelona area.

Explore text mining and natural language processing in rs to extract topic and hashtag tweets such as Jerusalem Embassy, locate users, geocode places, and visualize tweet origins on a map.

Learn to fetch location specific Twitter trends by identifying a place’s woeid, look up New York's id, and retrieve current trends with the get trends function.

Analyze followers and friends of the IUCN Red List Twitter handle with tidyverse and tidytext in R, extracting top followers, their statuses, and top tweets for today.

Learn to extract tweets with the rtweet package in R by authenticating with app keys and querying English tweets on a topic for text mining.

Learn how to extract geolocated tweets with rtweet, stream London tweets for 60 seconds, convert to a data frame, and analyze text, coordinates, and trends.

Install and load the rtweet package, configure the api key, api secret, and consumer key, and authenticate via your browser. Run search_tweets for North Korea in English to fetch tweets.

Authenticate with the tweet package, search 500 users tweeting the hashtag, and plot the top locations from the location column, notably Washington, D.C.

Learn to mine GitHub data with R by using the GitHub API to retrieve repository information, set up a GitHub app, and parse JSON into a data frame.

Register your app on Foursquare developers, obtain a client id and client secret, install the orkun package from GitHub, and authenticate to access venue tips via the API.

Learn to extract venue reviews and check-ins from the Foursquare API using R, focusing on Indian restaurants in Copenhagen, and analyze user tips and comments.

Explore how to extract and analyze textual data from major social media platforms, including Facebook Graph API, Twitter, Foursquare, and GitHub, to uncover trends and build predictive models.

Explore tweet data from Hillary Clinton and Donald Trump using a preprocessed dataset to reveal original versus retweeted content and reply activity.

Master standard text preprocessing in R using the famous package: lowercase, remove punctuation, strip whitespace and numbers, remove stop words, and stemming to form a document matrix.

Perform exploratory data analysis on tweets by polarity, separating positive and negative subsets, build a document-term matrix, identify frequent terms and a word cloud, cluster with k-means into five groups.

Explore a multiple document corpus of UK immigration manifestos with the Goldkorn data package in R, build a corpus and a document frequency matrix with top features.

Explore tidytext basics by converting Jane Austen texts to tidy data, tokenizing into words or sentences with unnest_tokens, detecting chapters via regex, and examining Pride and Prejudice as an example.

Explore and visualize text from Pride and Prejudice using tidytext in R, including tokenization, stop-word removal, word clouds, and sentiment analysis with the bing lexicon.

Explore multiple texts with tidytext by loading seven Harry Potter novels, tokenizing chapters, removing stop words, and analyzing word frequencies, correlations, and visualizations across books.

Count unique words in climate change tweets using the tweet package in R, clean text by removing http and stop words, convert to lowercase, and visualize the top 15 words.

Learn to compute and visualize tf-idf scores to reveal defining words across Jane Austen texts using tidytext in R, highlighting distinctive terms per book.

Visualize tf-idf and idf across Gutenberg texts, using tidytext to tokenize and plot the most distinctive words by author.

Conclude section six by exploring unstructured text with exploratory data analysis and visualizations of tweet and textual data, quantifying word frequency and introducing the teletext package for idf.

Explore how word clouds visualize tweet sentiments about India's 2016 demonetization, using pre-processing, corpus construction, and frequency-based sentiment analysis in R.

Learn to create word clouds from Amazon product reviews by preprocessing text, building a document-term matrix, and generating score-specific visuals to reveal frequent words such as flavor and good.

Learn to generate tidy word clouds from tweets and reviews by cleaning text, tokenizing and stemming with Snowball, removing stop words and URLs, and counting words for visualization.

Create word clouds with quanteda by building a corpus and document frequency matrix, then clean text with stemming and stop-word removal for immigration manifestos and tweets.

Learn to compute word frequency from text data in R, cleaning and converting to a corpus, then analyze Twitter data with API keys and plot the most frequent terms.

Apply a sentiment analysis workflow in R to Mugabe tweets, revealing anticipation and trust as the dominant emotions.

Explore sentiment analysis with tidytext, cleaning Jane Austen's Emma, convert to tidy format, compute word-level sentiment using bing lexicon, and visualize positive and negative terms.

Explore text polarity by calculating negative, neutral, and positive content with the cued up library on Mugabe tweets, revealing overall negativity and extracting positive and negative keywords.

Examine tweet polarity using an R workflow to assess individual tweet sentiment across about 6,000 posts from the Jerusalem embassy hashtag, revealing positive, negative, and neutral scores.

Demonstrates topic modelling with LBA in R to automatically uncover two themes from tweets, after cleaning text, removing multilingual stop words, stemming, and building a document matrix.

Download four public-domain novels, tokenize chapters, and build a four-topic lda model in R to distinguish the books by themes.

Use quanteda to model topics in tweets, cleaning text, building a dfm, converting to a dtm, and running a 20-topic lda with gibbs on Trump tweets.

Visualize text and social media data with word clouds and tidytext, build document-term matrices, and perform sentiment analysis and topic modeling for machine learning insights.

Cluster text data with hierarchical unsupervised classification on Catalan tweets about Catalonia. Build a document-term matrix, filter sparse terms, and apply Ward's method to reveal word clusters.

Cluster tweets with quanteda by building a document-feature matrix, selecting the top 50 words via idf, and performing hierarchical clustering to reveal word groupings like fake news and media.

Apply regression on textual data in R to predict book ranks from long descriptions, using corpus, document-term matrix, cross-validation, and glmnet with lambda tuning; results indicate strong predictive performance.

Apply supervised classification in R to distinguish ham from spam emails, building a document-term matrix from cleaned text and achieving about 94 percent accuracy.

Explore the RTextTools library for text classification and regression on congress data, build a document-term matrix, clean text, train models, classify results, and analyze analytics.

Use Artex tools to create a document matrix from email text and classify ham versus spam with a linear support vector machine. Split data 75/25 and predict unseen emails.

Learn to apply a deep learning framework for supervised text classification using the doc to work workflow and text to work package, with vector space modeling and support vector machines.

Learn to predict binary movie sentiment from reviews by converting text into a tf-idf document-term matrix and applying a binomial glmnet classifier with cross-validation, achieving high auc on unseen data.

Explore multiclass classification on text data using R, including encoding removal, tokenization, TF-IDF vectorization, and training a support vector machine with tenfold cross-validation and evaluation via a confusion matrix.

Explore a small social network by constructing an edge list, visualizing the graph, and interpreting connections among nodes like Jenny, Dave, Pete, and John.

Explore network analysis techniques to investigate and visualize interlinking relationships, using nodes and edges, directed versus undirected graphs, and edge lists in social networks.

Build a network from the correspondence data by defining nodes and edges and weighting edges by the number of letters, then visualize with tidygraph and ggraph.

Analyze a real life email network by building a sparse adjacency matrix from sender and receiver data and plotting a Komada Kawachi layout to identify key actors, including Hillary Clinton.

Explore network visualization with an inbuilt karate club dataset, constructing a graph from an adjacency matrix, applying igraph, layout options, and faction-based coloring to reveal two networks.

Extract 250 tweets for a hashtag using Social Media Lab and Margaretha, build an actor network and semantic term network, identify communities and clusters, and visualize user-hashtag associations.

Explore building a keyword co-occurrence network from NASA metadata using R, including data cleaning, extracting titles and descriptions, computing word pairs, and visualizing the network.

Build a network of words from Emma by constructing a document-term matrix, converting it to a matrix, and visualizing a weighted undirected adjacency graph with layout Frodsham Rheingold.

Learn how to use GitHub and GitHub Desktop to upload local assets, data, and code to a repository, create repositories, and push, fetch, and pull for collaboration.

Enable R in Google Colab with the rpy2 IPython extension, mount your drive, and run R code in cells to load libraries and read external data.

Explore data science as an interdisciplinary field that processes data with visualization, statistics, and machine learning. Prepare for Python-based roles by mastering exploratory data analysis and core building blocks.

Explore a data editing package in R that offers excel-like editing for data frames via a shiny app, enabling editing, filtering rows, and saving to csv.

Posit lets you deploy and share data science projects from your browser, with no install, using RStudio or Jupyter, and supports R or Python apps like Shiny, Streamlit, and Dash.

Learn how to choose the right database by applying a quick rule to SQL data when deciding between SQL and NoSQL databases.