Since re-starting my blog with a focus on architecture, PaaS, SOA, API Management, Big Data, and DevOps, Â I have seen increasing visitor interest in my content. Â Â With a super-majority of my visitors arriving via organic search, understanding how blog page meta-data and content aligns with referral search terms and page rank will help amplify my message. Â Â I’ve started to use wordle.netÂ to analyze keyword distribution on my blog pages,Â and tagcrowd.com to analyze search referral term distribution. Â The tools generate tag clouds that visualize keyword/term distribution.
A first pass simply collates search referral terms and does not normalize the list by search referral count. Â A visitor search for PaaS 50 times is counted equally as a search for SOA five times. Â In the tag cloud, the tag size for the search term indicates how many times the keyword is used in combination with other search term modifiers. Â For example, Â PaaS architecture, PaaS TCO, and DevOps PaaS. Â Figure 1 below illustrates the search term distribution:
Across all search term phrases, the tag cloud identifies the following common terms: ‘API’, ‘architecture’, ‘cloud’, ‘Enterprise Service Bus’, ‘ESB’, ‘paas’, ‘products’, and ‘vs WSO2′.
I used wordle.net to generate a tag cloud by evaluating keyword distribution across the cobiacomm RSS feed. Â A first pass limitation, the RSS feed only feeds blog post excerpt text up to the ‘read more…’ section break. Â Â Figure 2. below presents a tag cloud illustring keyword distribution across the cobiacomm RSS feed.
In the excerpt text, the blog post content demonstrates a focus on ‘architecture’, ‘application’, ‘business’, ‘cloud’, ‘open’, ‘platform’, ‘PaaS’, ‘source’, and ‘WSO2′.
Next pass will be to analyze full blog post content and properly weight referral search terms by term count.