An Earth Quake with a magnitude of about 5.7 in the Richter scale had just struck Northern California today. I quickly ran one of the Twitter mining and sentiment analysis program that I had developed to get various metrics of the sentiment.
This test drive was from a fairly small representative samples which is about 1500 tweets
Here is a video of my program run
Just to interpret the results
Tweets Sentiment Vibe!! [Score between -1.0 and 1.0 range] is–: 2%.
At 2% , This indicates that the sentiments are low , being a grave incident
Tweet’s Objective Perceptions –: 5% . This indicates that the perceptions are more of subjective than objective which is ok since many tweeters are not expected to be on ground zero and its still mid night in the US
Tweet’s Degree of Certainity :- 81% , This reflects that the nature of tweets indeed reflects the seriousness and certainty of the content related to the topic
The Tweets Positivity is –: 4% – This reflects lower level of a positivity , there is something to suspect or something seems to be obviously wrong or closer to negativity
The below metric reflects the mood of the tweets , which directly reflects a strong belief which is significantly higher at 1357 meaning that “It is indeed happening and a fact” as against a probable or imaginary belief
(‘The *Belief Mood* is :-‘, 1357)
(‘The *Probable or Imaginary Mood* is :-‘, 7)
These are sample metrics with representative results with just 1500 tweets with certain parameter thresholds. However, My belief is the tweet metrics and correlation perhaps does reflect the state of moods,perception / sentiments on the searched text ‘Northern California’ affected by an earth quake (which was trending high on twitter)
This software was not run on the cloud, but obviously when I intend to do an sentiment analysis on a larger scale from the twitter fire hose, I plan to use AWS Dynamo DB and Elastic search integrated to my core sentiment analyser software.