Text, sentiment, and social analytics help you tune in, at scale, to the voice of the customer, patient, public, and market. The technologies are applied in an array of industries — in healthcare, 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.
The analytics state-of-the-art is pretty good although uptake in certain areas — digital analytics and market research are examples — has lagged. But even in areas of strong adoption such as customer experience and social listening and engagement, there’s room for growth, for both technical innovation and more-of-the-same uptake. This market space means opportunity for new entrants and established players alike.
We could examine each analytics species in isolation, but better to look at the full, combined impact area. The technologies and applications overlap. Social analyses that neglect sentiment are incomplete, and to get at online, social, and survey sentiment, you need text analytics.
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 hold-outs remain — and true, it’s best to do one language really well than to cover many poorly — machine learning (ML) and machine translation have facilitated the leap to multi-lingual, 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.
- Text analysis gains recognition as a key business-solution capability — for customer experience, market research and consumer insights, digital analytics and media measurement — and providers will increasingly compete on their analytics. Build or
buysubscribe: 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.
- Machine learning, stats, and language engineering coexist. Tomorrow belongs to deep learning — to recurrent neural networks and the like — but today long-established language-engineering approaches still prevail. I’m referring to taxonomy, parsers, lexical and semantic networks, and syntactic-rule systems. (Two of my consulting clients are commercializing in these areas: eContext, providing taxonomy based classification infrastructure, and Contextors, implementing a very-high-precision English-language parser.) So we have a market where “a thousand flowers bloom, a hundred schools of thought contend…” and even co-exist. Cases in point: Even crowd-sourcing standard-bearer CrowdFlower is embracing machine learning, and start-up Idibon makes a selling point of combining traditional and 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 the tech to deciphering 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, recognizing the extent of the opportunity.
- A break-out for speech analytics, with video to come. The market loves to talk about omni-channel analytics and about the customer journey, involving 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 break-out adoption in 2016, beyond the contact center, by marketers, publishers, and research & insights professionals and 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 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.
- ISO emoji analytics. Given text, image, speech, and 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 (ROTFL!) and Facebook is experimenting with emoji Reactions, and more of a good thing: we’re seeing variants like Line stickers. Needed: emoji analytics. The tech is emerging, via start-ups 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 them, for instance SwiftKey, deserve a look. More to come in 2016!
- Deeper insights from networks plus content 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, D3.js, and Gephi (to name a few open-source options) are doing well, and building on a data-analytics platform such as QlikView is also an option — to be applied in conjunction with text and digital analytics: A to-do item for 2016.
- In 2016, you’ll be reading (and interacting with) lots more machine-written content. The technology is called natural language generation (NLG); the ability to compose articles — and e-mail, text messages, summaries, and translations — algorithmically from text, data, rules, and context. NLG is a natural for high-volume, repetitive content — think financial, sports, and weather reporting, and check out providers Arria, Narrative Science, Automated Insights, Data2Content, and Yseop — and also to hold up 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 would be a solved problem 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!
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.