Posts Tagged ‘sentiment analytics’

Alta Plana Takes The Pulse Of Text Analytics

wordcloudSeth Grimes, president and principal consultant of Alta Plana Corp. and founding chair of the Sentiment Analysis Symposium, has put together a thorough new report, Text Analytics 2014: User Perspectives on Solutions and Providers. Among the interesting findings of the report is that “growth in text analytics, as a vendor market category, has slackened, even while adoption of text analytics, as a technique, has continued to expand rapidly.”

Grimes explains that in a fragmented market, consisting of everything from text analytics services to solution-embedded technologies, the opportunities for users to practice text analytics is strong, but that increasingly text analytics is not the main focal point of the solutions being leveraged.

Reflecting the diversity of options, respondents listed among their providers a number of open-source offerings such as Apache OpenNLP and GATE, API services such as AlchemyAPI and Semantria, and enterprise software solution and business suite providers like SAP. The word cloud above was generated by Alta Plana at Wordle.net to show how users responded to the question of companies they know provide text/content analytics functionality. Nearly 50 percent of users are likely to recommend their most important provider.

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Corporate Social Media: Sentiment Tracking Is Up, But Other Metrics Are Out of the Mix

insightThe State of Corporate Social Media is … well, strong might be too strong a word for it. The recently released State of Corporate Social Media Briefing 2014, from USM (Useful Social Media) finds – among other things – that social media team sizes are being reduced, fewer budgets are increasing, and fewer key performance indicators are being measured.

It’s not all negative. In fact, report author and USM founder Nick Johnson concludes that all together, its results could be interpreted as indicating that “social media within companies is beginning to mature, and the drive to leverage social to its full extent is undiminished.”

That said, the data equally could be interpreted to mean that “social media within companies is stagnating, and there’s an increasing lack of resources available to those within business to move forward to full leverage social’s potential.”

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Daedalus Takes Meaning-As-A-Service To Excel, GATE And CMS Systems

meaningasaserviceDaedalus (which The Semantic Web Blog originally covered here) has just made its Textalytics meaning-as-a-service APIs available for Excel and GATE (General Architecture for Text Engineering), a JAVA suite of tools used for natural language processing tasks, including information extraction in many languages. Connecting its semantic analysis tools with these systems is one step in a larger plan to extend its integration capabilities with more API plug-ins.

“For us, integration options are a way to lower barriers to adoption and to foster the development of an ecosystem around Textalytics,” says Antonio Matarranz, who leads marketing and sales for Daedalus. The three main ecosystem scenarios, he says, include personal productivity tools, of which the Excel add-in is an example, and NLP environments, of which GATE is an example. “But UIMA (Unstructured Information Management Applications) is also a target,” he says. The list also is slated to include content management systems and search engines, among them open source systems like WordPress, Drupal, and Elasticsearch.

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Big Data Challenges In Banking And Securities

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

A new report from the Securities Technology Analysis Center (STAC), Big Data Cases in Banking and Securities, looks to understand big data challenges specific to banking by studying 16 projects at 10 of the top global investment and retail banks.

According to the report, about half the cases involved e petabyte or more or data. That includes both natural language text and highly structured formats that themselves presented a great deal of variety (such as different departments using the same field for a different purpose or for the same purpose but using a different vocabulary) and therefore a challenge for integration in some cases. The analytic complexity of the workloads studied, the Intel-sponsored report notes, covered everything from basic transformations at the low end to machine learning at the high-end.

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Load-Control: Semantria Takes On The Social Media Surge Infrastructure Challenge

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Image courtesy: Flickr/Webtreats

Semantria is tackling some of the challenges that come with being a cloud-based social media services provider startup. The company offers a text and sentiment analysis service (which you can read about here) to clients and partners. That includes companies like Sprinklr, which manages the social customer experience for other brands with the help of Semantria’s API for analyzing social signals about those clients.

The good news is that with growing social data volumes, there’s a growing need for semantically-oriented services like Semantria’s that help businesses make sense of that information for themselves or their clients. The downside is that a huge surge in volume of social mentions around a company, its product, or anything else can hit such services hard in the pocketbook when it comes to acquiring the cloud infrastructure to handle the tidal wave.

“Everybody suffers from this kind of thing,” says Semantria founder and CEO Oleg Rogynskyy. “We experience it daily.”

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SNAP To It: Dell Proposes Way Of Seeing Returns On Social Media Investments

shreeAre you seeing a return on your investment in social media? When the question about whether such returns exist was put to the audience at the recent Sentiment Analytics Symposium by Shree Dandekar, Dell Software’s chief strategist and senior director of BI and analytics, only a few hands went up. But Dandekar explained that it’s more possible to realize returns than many people may believe.

Dell’s Social Net Advocacy Pulse, or SNAP, tool and program is designed to help drive those returns. “Social ROI is not a myth but a reality,” Dandekar said. “It starts with a social media strategy and text analytics is a crucial underpinning of that journey,” which takes a company from social media listening and monitoring, to capturing and aggregating data from that, to engaging on and deriving insights from social media, to bringing that information into context with enterprise data for better lead- and opportunities-tracking. Dell is an in-house user of SNAP, bringing in 25,000 to 30,000 conversations a day for dell, he noted, and it runs a Social Media Command Center for facilitating listening to those conversations.  (It also helps customers implement their own Command Centers.)

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Bring On The Marketing Experts In Social Analytics

priusLast week The Semantic Web Blog reported on hoped-for improvements in the sentiment and text analytics space, a topic of discussion at this month’s Sentiment Analytics Symposium, including making the tools and data more accessible to business users – at least to those working outside of the market research sector. Within that space, experts continue to bring value.

Speakers at the event provided examples of how expertise at social analytics matters in the marketing realm, including David Rabjohns, CEO of MotiveQuest, which offers an online anthropology approach to helping brands identify the social “tribes” who may be targets for their products or services, given an understanding of what it is those tribes are most passionate about and a way that the brand can connect with those passions.

Take the case of the company’s work with Toyota on its Prius hybrid car: Rabjohns said that MotiveQuest’s online social research for the company revealed that Toyota would be mistaken to think it was selling a car and talking to users about saving money on gas.

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Take Sentiment Analytics And Make It Better, Better, Better, Better, Better, Oh

hjThere’s room for improvement in sentiment and text analytics technology: Greater reliability, more accessibility, and increased businesss actionability should be on the agenda.

“There are still tools that are crashing in the middle of a brand crisis or there’s not adequate support or there’s no integration with other tools that need the data,” Chris Boudreaux, global lead, social media and text analytics at Accenture, told an audience gathered at last week’s Sentiment Analysis Symposium. “The industry has to grow up and be more accountable in delivering tools and services.”

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Cruxly Analytics Technology Drives Actions From Intents

Image courtesy: Flickr/ M4D GROUP

Image courtesy: Flickr/ M4D GROUP

What are your customers – or potential clients – saying or asking online, often in short texts and streaming posts, or in emails about your products, services, or their own particular interests or desires? If you can understand their actionable intents in realtime, then you have a good shot at responding swiftly and appropriately to those expressed intents, requests, or queries. That could add up to new sales, new customers, and better marketing and product management.

Startup Cruxly, which is presenting at this week’s Sentiment Analysis Symposium in NYC, believes it’s taking the oft-touted concept of social media monitoring in a new direction with its platform that applies natural language processing techniques for intent detection in realtime. “The idea is to be actionable,” says CEO Aloke Guha. “If it’s not actionable, at most [monitoring] is a nice-to-have [capability].”

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Plumbing The Depths Of Deep Learning

Image Courtesy: Flickr/ Hey Paul Studios

Image Courtesy: Flickr/ Hey Paul Studios

Will deep learning take us where we want to go? It’s one of the questions that Oxford University professor of Computational Linguistics Stephen Pulman will be delving into at this week’s Sentiment Analysis Symposium. There, he’ll be participating in a workshop session today on compositional sentiment analysis and giving a presentation tomorrow on bleeding-edge natural language processing.

“There is a lot of hype about deep learning, but it’s not a magic solution,” says Pulman. “I worry whenever there is hype about some technologies like this that it raises expectations to the point where people are bound to be disappointed.”

That’s not to imply, however, that important progress isn’t taking place when it comes to deep learning, which leverages machine learning methods based on learning representations with applications to everything from NLP to computer vision to speech recognition.

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