Voice of the voter, voice of the customer and brand reputation management are just a few of the top applications for text analytics and information classification technologies.
Could sentiment analysis -- the automated mining of attitudes, opinions, and emotions from text, speech, and database sources -- have foretold the demise of Egyptian autocrat Hosni Mubarak?
Can this fast-emerging technology and discipline predict the movement of Oracle's share price based on online and social reactions to company and market news? Could it quantify reactions to Groupon's widely panned SuperBowl ad and tell the ad-agency creative types what particular aspects viewers disliked? And in a more mundane, everyday application, could it identify product defects to help convert dissatisfied customers into promoters?
These are four of seven sentiment-analysis scenarios that I'll describe, ways the technology can be (and is being) applied to derive insight from qualitative information sources.
Sensible Sentiment Solutions
In the pre-Net, pre-text analytics world, sentiment analysis was of very limited scope or limited to indirect measures. We had focus groups and other forms of "qualitative research" that, because they are expensive and generate voluminous text transcripts requiring laborious human analysis, can be used for only small samples.
Survey "verbatims" (free-text responses) are similarly expensive to analyze, so indicators such as the U.S. Consumer Confidence Index (CCI) have also been based on samples, with question responses limited to not-very-illuminating positive/negative/neutral responses.
Text analytics is a limit breaker. Solutions automate large-scale information collection, filtering, and classification technologies via natural language processing and data mining technologies that handle both factual and subjective information. Subjective information: That would be attitudes, moods, opinions, and emotions -- the province of sentiment analysis.
Where can automated sentiment analysis take us?
Consider these seven sentiment-analysis scenarios, or applications:
Voice of the Voter: Sentiment analysis will help political organizations, campaigns, and news analysts better understand which issues and positions matter most to voters. The technology was applied during the 2010 British and American national election campaigns. Expect it to break big later this year as 2012 U.S. presidential and congressional campaigns ramp up. Opinion extraction from companies such as Appinions will prove a real asset, a complement to the BI dashboard displays provided by vendors such as Crimson Hexagon.
Capital Markets Modeling : Market sentiment is part of the common vocabulary -- think bullish and bearish -- with attitudes inferred from news, trading patterns, technical indicators, and good-old gut feeling.
Traders are on a constant look-out for an edge, for insights and a jump on the market that provide advantage. Enter automated financial (and consumer) news analysis, with extracted sentiment corrected to market models, techniques applied by the Thomson-Reuters NewsScope sentiment engine, RavenPack, and by Digital Trowel The Stock Sonar. (Disclosure: RavenPack is a paying sponsor of the up-coming Sentiment Analysis Symposium, which I am organizing.) Expect uptake to accelerate dramatically as the economic recovery progresses.
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.
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