AI Feeling a Little Sentimental
July 24, 2017
Big data was one of the popular buzzwords a couple years ago, but one conundrum was how organizations were going to use all that mined data? One answer has presented itself: sentiment analysis. Science shares the article, “AI In Action: How Algorithms Can Analyze The Mood Of The Masses” about how artificial intelligence is being used to gauge people’s emotions.
Social media presents a constant stream of emotional information about products, services, and places that could be useful to organizations. The problem in the past is that no one knew how to fish all of that useful information out of the social media Web sites and make it a usable. By using artificial intelligence algorithms and natural language processing, data scientists are finding associations between words, the language used, posting frequency, and more to determine everything from a person’s mood to their personality, income level, and political associations.
‘There’s a revolution going on in the analysis of language and its links to psychology,’ says James Pennebaker, a social psychologist at the University of Texas in Austin. He focuses not on content but style, and has found, for example, that the use of function words in a college admissions essay can predict grades. Articles and prepositions indicate analytical thinking and predict higher grades; pronouns and adverbs indicate narrative thinking and predict lower grades…’Now, we can analyze everything that you’ve ever posted, ever written, and increasingly how you and Alexa talk,’ Pennebaker says. The result: ‘richer and richer pictures of who people are.’
AI algorithms are able to turn a person’s online social media accounts and construct more than a digital fingerprint of a person. The algorithms act like digital mind readers and recreate a person based on the data they publish.
Whitney Grace, July 24, 2017
Instagram Reins in Trolls
July 21, 2017
Photo-sharing app Instagram has successfully implemented DeeText, a program that can successfully weed out nasty and spammy comments from people’s feeds.
Wired in an article titled Instagram Unleashes an AI System to Blast Away Nasty Comments says:
DeepText is based on recent advances in artificial intelligence, and a concept called word embeddings, which means it is designed to mimic the way language works in our brains.
DeepText initially was built by Facebook, Instagram’s parent company for preventing abusers, trolls, and spammers at bay. Buoyed by the success, it soon implemented on Instagram.
The development process was arduous wherein a large number of employees and contractors for months were teaching the DeepText engine how to identify abusers. This was achieved by telling the algorithm which word can be abusive based on its context.
At the moment, the tools are being tested and rolled out for a limited number of users in the US and are available only in English. It will be subsequently rolled out to other markets and languages.
Vishal Ingole, July 21, 2017
Software That Detects Sarcasm on Social Media
July 20, 2017
Technion-Israel Institute of Technology Faculty of Industrial Engineering and Management has developed Sarcasm SIGN, a software that can detect sarcasm in social media content. People with learning difficulties will find this tool useful.
According to an article published by Digital Journal titled Software Detects Sarcasm on Social Media:
The primary aim is to interpret sarcastic statements made on social media, be they Facebook comments, tweets or some other form of digital communication.
As we move towards a more digitized world where the majority of our communications are through digital channels, people with learning disabilities are at the receiving end. As machine learning advances so do the natural language capabilities. Tools like these will be immensely helpful for people who are unable to understand the undertones of communication.
The same tool can also be utilized by brands for determining who is talking about them in a negative way. Now ain’t that wonderful Facebook?
Vishal Ingole, July 20, 2017
Study: Social Media and Young People
July 19, 2017
Some of us elders have been saying it for years, but now research seems to confirm it—social media can be bad for mental health. The Next Web reports, “Study: Snapchat and Instagram Are the Worst for Young People.” The study is from the UK’s Royal Society for Public Health (RSPH), and the “young people” sampled are 1,479 Brits aged 14-24. An explanatory three-minute video from the RSPH accompanies the article. Writer Rachel Kaser reports:
The researchers surveyed 1,479 British youths ages 14-24, asking them how they felt the different social media networks effected their mental health. They took in several factors such as body image, sleep deprivation, bullying, and self-identity. The results suggest the two worst social media networks for kids are Instagram and Snapchat, as they had terrible scores for body image, bullying, and anxiety. Twitter and Facebook weren’t much better, though. YouTube was the only one that apparently inspired more positive feelings than negative ones. It could be because Snapchat and Instagram are image-based apps, meaning it’s not easy for users to avoid visual comparisons. Both apps ranked high on ‘Fear of Missing Out,’ and the researchers suggested this was likely to foster anxiety in fellow users.
I recommend the video for interested readers. It shows some respondents’ answers to certain questions, and clearly summarizes the pros and cons of each platform examined. It helpfully concludes with a list of concrete suggestions: Implement pop-up notifications that tell users when they’ve been online for a certain amount of time; require watermarks on photos that have been digitally altered; educate folks on the healthy use of social media; and incorporate analysis tools to identify users at risk for poor mental health and “discreetly” steer them toward help. It does seem such measures could help; will social-media companies cooperate?
Cynthia Murrell, July 19, 2017
The New York Times Pairs up with Spotify for Subscription Gains
July 18, 2017
The article on Quartz Media titled The New York Times Thinks People Will Still Pay for News—
If Given Free Music examines the package deal with Spotify currently being offered by the Times. While subscriptions to the news publication have been on the rise thanks in large part to Donald Trump, they are still hurting. The article points out that if the news and music industries have one thing in common, it is trying to get people to pay for their services.
The two companies announced an offer… giving a free year of Spotify Premium to anyone in the US who signs up for an all-access subscription to the news publication. Premium normally costs $120 a year, and the offer slashes the price of an all-access Times subscription too—from $6.25 a week to $5 a week… While it may seem like both companies will take a hit from these discounts, the boost in new subscribers/readers will likely more than make up for it.
It is a match made on Tinder, a coupling for the new world order. Will this couple get along? As millennials seek new outlets for activism, purchasing a subscription to the Times is a few steps above posting a rant on Facebook. Throw a year of Spotify into the mix and this deal is really appealing to anyone who doesn’t consider the Times a “liberal rag.” So maybe the Donald won’t be interested, but the rest of us sure might consider paying $5/month for legitimate news and music.
Chelsea Kerwin, July 18, 2017
Bing Focuses on Chatbots
June 21, 2017
Chatbot enthusiasts may want to turn to Bing, because now “Bing Makes it Easier to Find Chat Bots,” according to SearchEngine Journal.” Writer Matt Southern reveals:
Bing has released an update designed to make it easier to find chat bots for instant messaging platforms. Searching for a command such as ‘travel bots’ will return a dedicated answer box where you can browse through chat bots for Facebook, Skype, Slack, and Telegram. Bots can be added to messaging platforms directly from search results by clicking on the ‘Add bot’ button. Bing is piloting a test program which allows searchers to interact with chat bots on Bing itself. Searching for specific restaurants in the Seattle area can return a dedicated bot which you can chat with for more information about the restaurant.
Bing hopes to expand the restaurant service to more cities “eventually.” Meanwhile, they have been developing an InfoBot to answer users’ questions with entries from Wikipedia and, later, from other information sites like WebMD and AllRecipies. We’re also told that developers can use the Microsoft Bot Framework to design Bing chatbots, which may be made available to users after a review process.
Cynthia Murrell, June 21, 2017
Snapchat Introduces Search Feature
May 29, 2017
Photo-sharing app Snapchat is late to the search game, but it has now arrived. The Daily Mail reports, “Snapchat Introduces a ‘Universal Search’ Feature: Tool Lets You Create Groups and Find New People to Follow.” Writer Abigail Beall explains:
Snapchat’s universal search bar hopes to address an issue some users had with the photograph-sharing app – the difficulty in finding new people to follow and gaining a large following. Previously, the only way people could gain a following was by sharing their username, or Snapcode, outside of the app. The new search bar, that will always be present at the top of the app, will allow people to find users easily through searching, discovering and groups. …
The new feature also lets users create groups, to combine snaps. Previously, boxes for finding specific conversations, accounts to follow and Stories or Discover channels were all in different places.
The tool was implemented for some Android users in mid-January, with availability to all Android and iOS users to follow “soon.” Beall notes the development was predicted by some last August after Snapchat acquired Vurb, a mobile search startup founded in 2011 and based in San Francisco.
Snap Inc., Snapchat’s parent company, bills itself as a camera company that is reinventing the camera. The company has acquired nine other enterprises since its founding in 2011. Snap is now selling (through their special vending machines!) Spectacles, sunglasses with a camera on each temple that, of course, link right in with Snapchat.
Cynthia Murrell, May 29, 2017
Elastic Search Redefining Enterprise Search Landscape
May 24, 2017
Open source enterprise search engine Elastic Search is changing the way large IT enterprises are enabling its user to search relevant data in a seamless manner.
Apiumhub in an in-depth report titled Elastic Search; Advantages, Case Studies & Books says:
Elastic search is able to achieve fast search responses because, instead of searching the text directly, it searches an index instead. This is more or less like searching for a keyword by scanning the index at the back of a book, as opposed to searching every word of every page of the book.
The search engine is easily scalable and can accommodate petabytes of data on multiple servers in short time. Considering it is based on Lucene, developers too find it easy to work with. Even if the keywords are misspelled, the search engine will correct the error and deliver accurate results.
At present, large organizations like Tesco, Wikipedia, Facebook, LinkedIn and Salesforce have already deployed the enterprise search engine across their servers. With the advent of voice-based search, capabilities of Elastic search will be in more demand in the near future, experts say.
Vishol Ingole, May 24, 2017
Machine Learning Going Through a Phase
May 10, 2017
People think that machine learning is like an algorithm magic wand. It works by some writing the algorithmic code, popping in the data, and the computer learns how to do a task. It is not that easy. The Bitext blog reveals that machine learning needs assistance in the post, “How Phrase Structure Can Help Machine Learning For Text Analysis.”
Machine learning techniques used for text analysis are not that accurate. The post explains that instead of learning the meaning of words in a sentence according to its structure, all the words are tossed into a bag and translated individually. The context and meaning are lost. A real world example is Chinese and Japanese because they use kanji (pictorial symbols representing words). Chinese and Japanese are two languages, where a kanji’s meaning changes based on the context. The result is that both languages have a lot of puns and are a nightmare for text analytics.
As you can imagine there are problems in Germanic and Latin-based languages too:
Ignoring the structure of a sentence can lead to various types of analysis problems. The most common one is incorrectly assigning similarity to two unrelated phrases such as “Social Security in the Media” and “Security in Social Media” just because they use the same words (although with a different structure).
Besides, this approach has stronger effects for certain types of “special” words like “not” or “if”. In a sentence like “I would recommend this phone if the screen was bigger”, we don’t have a recommendation for the phone, but this could be the output of many text analysis tools, given that we have the words “recommendation” and “phone”, and given that the connection between “if” and “recommend” is not detected.
If you rely solely on the “bag of words” approach for text analysis the problems only get worse. That is why it phrase structure is very important for text and sentiment analysis. Bitext incorporates phrase structure and other techniques in their analytics platform used by a large search engine company and another tech company that likes fruit.
Whitney Grace, May 10, 2017
Motivations for Microsoft LinkedIn Purchase
April 13, 2017
We thought the purchase was related to Microsoft’s in-context, real-time search within an Office application. However, according to BackChannel’s article, “Now We Know Why Microsoft Bought LinkedIn,” it’s all about boosting the company’s reputation. Writer Jessi Hempel takes us back to 2014, when CEO Satya Nadella was elevated to his current position. She reminds of the fiscal trouble Microsoft was having at the time, then continues:
It also had a lousy reputation, particularly in Silicon Valley, where camaraderie and collaboration are hallmarks of tech’s evolution and every major player enjoys frenemy status with its adversaries. Microsoft wasn’t a company that partnered with outsiders. It scorned the open-source community and looked down its nose at tech upstarts. In a public conversation with Marc Andreessen in October 2014, investor Peter Thiel called Microsoft a bet ‘against technological innovation.’
The write-up goes on to detail ways Nadella has turned the company around financially. According to Hempel, the LinkedIn purchase, and the installation of its founder Reid Hoffman on the board, are in an effort to boost Microsoft’s reputation. Hembel observes:
As a board member, Hoffman will be Microsoft’s ambassador in the Valley. Among a core group of constituents for whom Microsoft may not factor into conversation, Hoffman will work to raise its profile. The trickle-down effect has the potential to be tremendous as Microsoft competes for partners and talent.
See the article for more information on the relationship between the Nietzsche-quoting Nadella and the charismatic tech genius Hoffman, as well as changes Microsoft has been making to boost both its reputation and its bottom line.
Cynthia Murrell, April 13, 2017
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