Keywords: wordcloud2, quanteda, reactable Lastly, the app leverages quanteda for multi-word keyword detection and for providing context around a keyword of interest. Furthermore, once the user has identified the most frequent keywords in the source text, the app allows the user to quickly get context by providing a window of the words that come before and after the specified keyword.
Most word cloud generating apps only do single-word keywords and sometimes this can be limiting. The app automatically detects multi-word keywords and also allows the user to get context for a particular keyword.įull Description: The objective of the app is to generate a word cloud that takes into account multi-word keywords.
Use Ctrl+Left/Right to switch messages, Ctrl+Up/Down to switch threads, Ctrl+Shift+Left/Right to switch pages.Word Cloud generator with multi-word detectionĪbstract: The app allows the user to paste or upload text and generate a word cloud to quickly get insights into their text data. General News Suggestion Question Bug Answer Joke Praise Rant Admin At times, filtering for 'ing' is OK, at times not - or think about hard and hardly or burn and burnout Many other suffixes can alter the meaning of a word. But the outer join between your and my examples should be a good starter.
Regarding the collection of suffixes, I am not a native speaker. Meaning, I wouldn't focus on only one of these during parsing the input text but evaluate afterwards. only the one with the max count should make it into the cloud. Depending on the nature of the text, my guess is that it is OK to leave out string.length like 's, ing, s. At least the user should be able to specify the blacklisted words without coding & compiling - or did I overlook something?
I complained based on just using the binaries and looked at the code later on. In this version, I switched to the IWord interface. In the first implementation, I was using KeyValuePair to represent them. The result is an enumeration with pairs of terms (words) and integers representing the number of occurrences of this word in a text. To tap your own data source, just implement the IEnumerable interface or derive from BaseExtractor.Ĭounting words and ignoring ones from blacklist. Another one UriExtractor fetches a URL content and tries to clean away HTML tags and JavaScript (to be honest, I just implemented it as a showcase and its filtering capabilities are very pure). FileExtractor is able to process large text files line by line. TextExtractor extracts all words from some text string ignoring spaces and all non-letter characters. As an example, I have implemented three of them. Processing data like text, HTML, or source code, and extracting the relevant words while ignoring others. These charts are similar to word clouds where words that occur more frequently are shown bigger. Word trees display how a set of selected words are connected to other words in text data with a branching layout.
There are four phases when visualizing the word cloud: In this tutorial, I will teach you how to quickly create nice interactive word tree charts using JavaScript. There were a number of components I found on the web, but most of them had either very pure performance when processing text and the visualization or the layout was not what I expected. I really loved the visualizations produced by Wordle, but my goal was to write a non web based local solution to process large amounts of sensible data. In fact, the control is a screw-out product of my project at.
This control is inspired by the free web-based word cloud generator called Wordle.