Tag Cloud (visualizing bias and areas of commitment) by Serena CarpenterWordlehttp://www.wordle.net/1. Select Create2. Select URL to visualize or text to visualize3. Edit Layout, Font, Color4. Copy and paste code5. Save to gallery6. When you click on image, it will take you to the Wordle site

http://psychology.wichita.edu/surl/usabilitynews/111/tagcloud.asphttp://taporware.ualberta.ca/~taporware/betaTools/wordcloud.shtml

Voyanthttp://voyant-tools.org/ 1. Copy and paste text or url2. Frequent, unique words, and word cloud

OpinionCrawlhttp://www.opinioncrawl.com/Sentiment analysis of topics across the webSentiment Analysis with Python NLTK Text Classificationhttp://text-processing.com/demo/sentiment/Sentiment analysis of blocks of text (50,000 characters)

Sentiment140http://www.sentiment140.com Sentiment analysis of keywords on TwitterSentiment Vizhttp://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/ Recent tweets that contain your keyword are pulled from Twitter and visualized the Sentiment tab as circles.

Easy Text Classification with Machine Learning (etcML) http://www.etcml.com/1. Predict > Search for Tweets 2. In search bar > (hashtags ((#sxsw) for positive, negative, and neutral sentiment analysis)

3. Predict > New Datasets > Dataset Name (descriptive-year) > Dataset Description (Description of project) > Check both private (if wanted) > Check label if you are uploading a labeled dataset > Add Classifier > Tag project4. Copy and paste text or upload text file > Create dataset > Select classifier > Select the most popular, which is the sentiment analysisa. Useful qualitative interview analysis5. Or can create your own labels for the project in a .txt file (not necessary)a. posI liked this.b. posI sort of liked itc. posI liked this movie.d. negI hated this movie.e. negWe all sort of disliked it.f. negWe disliked the whole story.g. http://www.etcml.com/advanced-tutorial