Text, sentiment, and social analytics help you tune in, at scale, to the voice of the customer, patient, public, and market. The technologies are currently being applied in an array of industries ranging from healthcare to finance, media, and consumer markets. They distill business insight from online, social, and enterprise data sources.
It’s useful stuff, insight extracted from text, audio, images, and connections.
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We could examine each analytics area in isolation, but it’s better to look at the combined impact, as the technologies and applications overlap. Social analyses that neglect sentiment are incomplete, and to get at online, social, and survey sentiment, you really need text analytics.
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This article, a look-ahead technology and market assessment, surveys high points for the year to come, with a run-down of 10 text, sentiment, and social analytics trends to watch for in 2016.
Multi-lingual is the rule
While English-only analytics holdouts remain, and it’s certainly better to do one language really well than to cover many poorly, machine learning (ML) and machine translation have facilitated the leap to multi-lingual analytics, making it the new norm. But if you do need to work across languages, do some digging: Many providers are strong in core languages but weak in others. Choose carefully.
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Text analysis gains recognition
Text analysis capability is a key solution for customer experience, market research, and consumer insights, and for digital analytics and media measurement — and providers are increasingly competing on the merits of their analytics. Build or subscribe: both are viable options. While you could call this trend point quantified qualitative, what really matters is that text analysis is baked into the business solution.
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Machine learning, stats, and language engineering coexist.
Tomorrow belongs to deep learning — to recurrent neural networks and the like — but for today, long-established language-engineering approaches still prevail. I’m referring to taxonomy, parsers, lexical and semantic networks, and syntactic-rule systems. So we have a market where “a thousand flowers bloom, a hundred schools of thought contend…” and these many approaches can coexist. Cases in point: Even crowd-sourcing standard-bearer CrowdFlower is embracing machine learning, and startup Idibon makes a selling point of combining the traditional and the new: “You can construct custom taxonomies and tune them with machine learning, rules, and your existing dictionaries/ontologies.”
Image analysis enters the mainstream.
Leading-edge providers are already applying image analysis tech to decipher brand signals in social-posted media — check out Pulsar and Crimson Hexagon — and image analysis ability, via deep learning, was a major selling point in IBM’s 2015 AlchemyAPI acquisition. Indeed, hot ML start-up MetaMind pivoted in 2015 from NLP to a focus on image analysis, as it recognized the extent of the opportunity.
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A breakout for speech analytics, with video to come.
The market loves to talk about omni-channel analytics and about the customer journey, which involves multiple touchpoints. And, of course, social and online media are awash in video. The spoken word — and non-textual speech elements, including intonation, rapidity, volume, and repetition — carry meaning, accessible via speech analysis and speech-to-text transcription. Look for breakout adoption in 2016, beyond the contact center, by marketers, publishers, and research and insights professionals. Expect speech analytics to also serve as an enabler for high-accuracy conversational interfaces.
Expanded emotion analytics.
Advertisers have long understood that emotion drives consumer decisions, but, until recently, broad, systematic study of reactions has been beyond our reach. Enter emotion analytics, either a sentiment analysis subcategory or sister category, depending on your perspective. Affective states are extracted from images and video via facial-expression analysis (or from speech or text), with the aim of quantifying our emotional reactions to what we see, hear, and read. Providers include Affectiva, Emotient, and Realeyes for video, Beyond Verbal for speech, and Kanjoya for text; adopters in this rapidly expanding market include advertisers, media, marketers, and agencies.
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ISO emoji analytics.
Given that we have text, image, speech, video, and Likes — why use emoji? Because they’re compact, easy to use, expressive, and fun! Like #hashtags, they complement and add punch to longer-form content. That’s why Internet slang is dead (ROFL!) and Facebook is experimenting with emoji Reactions, and — more of a good thing –we’re seeing variants like Line stickers. What’s needed now is emoji analytics. The tech for this area is emerging via startups such as Emogi. (Check out Emogi’s illuminating 2015 Emoji Report). Although most others don’t go beyond counting and classification to get at emoji semantics — the sort of analysis done by Instagram engineer Thomas Dimson and by the Slovene research organization CLARIN.SI — some of these, for instance SwiftKey, deserve a look.
Deeper insights from networks plus content
This is both a 2016 trend point and most of the title I gave to a 2015 interview with Preriit Souda, a data scientist at market-research firm TNS. Preriit observes, “Networks give structure to the conversation while content mining gives meaning.” Insight comes from understanding messages and connections and how connections are activated. So add a graph database and network visualization tools to your toolkit — there’s good reason Neo4j, js, and Gephi (to name a few open-source options) are doing well. Building on a data-analytics platform such as QlikView is another option, one that can be applied in conjunction with text and digital analytics: a to-do item for 2016.
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In 2016, you’ll be reading (and interacting with) lots more machine-written content.
The technology for machine-written content is called natural language generation (NLG) and provides the ability to compose articles — and email, text messages, summaries, and translations — algorithmically from text, data, rules, and context. NLG is a natural for high-volume, repetitive content: financial, sports, and weather reporting. Check out providers Arria, Narrative Science, Automated Insights, Data2Content, and Yseop. You can also look to the machine’s end of your conversation with your favorite virtual assistant — with Siri, Google Now, Cortana, or Amazon Alexa — or with an automated customer-service or other programmed response system. These latter systems fall in the natural-language interaction (NLI) category; Artificial Solutions is worth a look.
Machine translation matures.
People have long wished for a Star Trek-style universal translator, but while 1950s researchers purportedly claimed that machine translation was a problem that would be a solved within three or five years, accurate, reliable MT has proved elusive. (The ACM Queue article Natural Language Translation at the Intersection of AI and HCI nicely discusses the machine translation state of the human-computer union.) I wouldn’t say that the end is in sight, but thanks to big data and machine learning, 2016 (or 2017) should be the year that major-language MT is finally good enough for most tasks. That’s an accomplishment!
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Every one of these trends will affect you, whether directly — if you’re a text, sentiment, or social analytics researcher, solution provider, or user — or indirectly, because analysis of human data is now woven into the technology fabric we rely on every day. The common thread is more data, used more effectively, to create machine intelligence that changes lives.
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