Take a look at the state of sentiment analysis and explore future directions.
Whether in conversation or posted online or to our social networks, subjectivity and sentiment add richness to human communications. Captured electronically, customer sentiment--expressions that go beyond facts and that convey mood, opinion, and emotion--carries immense business value.
We're talking about the voice of the customer, and of the prospect, patient, voter, and opinion leader.
Listening--for brand mentions, complaints, and concerns--is the first element of any credible social engagement program. Businesses that listen can uncover sales opportunities, measure satisfaction, gauge reactions to marketing campaigns and message themes, uncover root causes behind events, and detect and respond to reputation and competitive threats. That's why we have monitoring and analytics solutions--the best of which apply text and sentiment analysis technology--targeting online and social media as well as enterprise feedback in surveys, e-mail, and contact center notes. These solutions are aimed at discovering business value in complex, expressive, and sometimes-confusing human language.
My aim here is to explore modalities, or how information technology helps us get at affect and attitudinal information. (These points will be covered in depth at a social media analysis and engagement conference that I organize: the Sentiment Analysis Symposium, October 30 in San Francisco.)
I'll start with a key definition: Sentiment analysis systematically rates human affective states according to positive or negative polarity or a neutral or mixed value, or according to mood, emotion, or feelings (angry, happy, sad, proud, disappointed, etc.) and to use sentiment data for business purposes.
Then we'll explore two questions: What types of sentiment analysis are there? And in what directions is sentiment analysis evolving?
"What type of person is she?" The question has many answers.
Each of us has many types, according to our demographic category (e.g., sex, age, race, income), personality, interests, occupation, and so on. We are many types simultaneously. Any question that takes the form, "What types of x are there?" has many answers, and when x = Sentiment Analysis, there's no exception.
Of course, we're most interested in the most important types of sentiment analysis. Here they are, as I see them:
Coarse-grained to fine-grained. Some analyses discern sentiment at a corpus or data-space level (e.g., for a set of reviews or survey responses); others score particular documents or messages; and others resolve sentiment at an entity (e.g., person, place, or company), topic, or concept level. Coarse-grained analysis is fine for some business applications, but others need fine-grained.
Individual versus aggregate. Analyses might look for individual cases ("mentions") or for aggregates over populations or sources or trends over time. If you're managing customer support, for example, you need to get at each mention, but if you're studying market pulse you're looking for the big picture.
Metric. Analyses may rate sentiment on an absolute scale, or they may look for relative/comparative sentiment--"I don't much care for sports, but I do prefer basketball to ice hockey"--and measure variation, intensity, and change.
I believe these three factors are underappreciated, underutilized measures. Too many tools rate a review with 8 positive points where 7 negative is mildly positive. But the high degree of variation should flag the review as interesting, even more so if there are both strong positives and strong negatives--that is, intense versus mild opinions. Sentiment change is always notable. It invites the question: What triggered the change?
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.
InformationWeek Must Reads Oct. 21, 2014InformationWeek's new Must Reads is a compendium of our best recent coverage of digital strategy. Learn why you should learn to embrace DevOps, how to avoid roadblocks for digital projects, what the five steps to API management are, and more.