According to Wikipedia, Sentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials. The rise of social media such as blogs (Blogger, WordPress, TypePad) and social networks (Facebook, LinkedIn, Ning) has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.

For example, if you are considering buying an Apple iPad, you can check latest Twitter sentiment (positive, negative or neutral) towards the product using online Twitter Sentiment site as below:

iPad Twitter Sentiment

Gadgets powered by Google
Other sites that offer such similar applications are:
1. TweetFeel – Tweetfeel monitors positive and negative feelings in twitter conversations about stuff like movies, musicians, TV shows and popular brands.
2. Twendz – Twitter-mining Web application uses the power of Twitter Search, highlighting conversation themes and sentiment of the tweets that talk about topics you are interested in.
3. PeopleBrowsr – PeopleBrowsr engages the collective intelligence in real-time conversation, identify top influencers, search cross network and see multiple accounts across social streams.
4. groubal – it tracks the customer dissatisfaction of hundreds of brands through social media. A customer satisfaction index built using a Bayesian classifier. Claims 90% accuracy.
5. Brandcrown – it ranks popular brands.
6. OpinionCrawl – online sentiment analysis for current events, companies, products, and people.
7. Tweet Sentiment – attempts to find a correlation between Twitter sentiment and stock prices.

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STATISTICA (Data Miner/Text Miner) has been gaining popularity among data miner/analyst around the globe these days. A report from the Rexer’s Annual Data Miner Survey in 2010 stated that STATISTICA Data Miner along with IBM SPSS Modeler and R received the strongest satisfaction ratings as a data mining tool in both 2010 and 2009; moreover, it was rated as the primary data mining tool chosen most often (18%). STATISTICA is a statistics and analytics software package developed by StatSoft. The software includes an array of data analysis, data management, data visualization, and data mining procedures; as well as a variety of predictive modeling, clustering, classification, and exploratory techniques. Hence, I have compiled some great resources about Statistica for your reference:

StatSoft – official Statistica creator site; more about the product, download and support.
Statistica in Wikipedia – provides history, overview and references of Statistica software package.
Statistica Video Tutorials – compilation of videos on how to getting started with Statistica; including links to Youtube Statistica channel.
UCLA Notes & Movies – provides hands-on experience using Statistica for doing statistics, graphics and data management. Recommended for starters/students.
Statistica Group – have a question? then join discussion group in Yahoo!Group.
Statistica Review – not sure to use Statistica, then read personal review of open source and commercial software packages for Data Mining; including Orange, R, RapidMiner, Statistica and WEKA.
Statistica in Amazon – shortcut to books related to Statistica in Amazon.com
Statsoft Statistica Books – additional statistical resources beyond the STATISTICA manual that accompanies the software?
Statistica Forum – ask a question or share your thoughts about Statistica.
Statistica Facebook – are you Statistica fan? then join their Facebook!

R Data Mining Resources

Data Mining using R has been gaining popularity among data miner/data analyst around the globe these days. A report from the Rexer’s Annual Data Miner Survey in 2010 stated that R has become the data mining tool used by more data miners (43%). According to Wikipedia, R is a programming language and software environment for statistical computing and graphics. R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. Thus, I have compiled top resources about R data mining for your reference:

R Project for Statistical Computing – the official R open source project website. Here you can get the latest release of R source code, manuals and recent bugs.
R Books Website – list of latest books that are related to R and may be useful to the R user community. You may also like to read Data Mining with R book (data mining bestseller at Amazon.com).
R in Wikipedia – here you can read basic info and example for R programming, including list of GUI for R and some references.
Rattle: A GUI for Data Mining using R – a simple and logical graphical user interface based on Gnome that can be used by itself to deliver data mining projects. Rattle runs under GNU/Linux, Macintosh OS/X, and MS/Windows.
R reference card for data mining – a collection of R packages and functions for data mining.
R Bloggers – a central hub of news and tutorials contributed by (185) R bloggers.
R Video Tutorials – a series of R for Statistical Programming screencasts that show you how to use R for for text mining. (some of the video links are missing)
Reasons to learn R? – YouTube video describing why students should learn the R programming language.
Programming R – online R programming resources from beginner to advanced resources.
R Programming Wikibook – a place where anyone can share his/her tricks and knowledge on R.