"sentiment analysis" entries

Four short links: 7 April 2015

Four short links: 7 April 2015

JavaScript Numeric Methods, Misunderstood Statistics, Web Speed, and Sentiment Analysis

  1. NumericJS — numerical methods in JavaScript.
  2. P Values are not Error Probabilities (PDF) — In particular, we illustrate how this mixing of statistical testing methodologies has resulted in widespread confusion over the interpretation of p values (evidential measures) and α levels (measures of error). We demonstrate that this confusion was a problem between the Fisherian and Neyman–Pearson camps, is not uncommon among statisticians, is prevalent in statistics textbooks, and is well nigh universal in the pages of leading (marketing) journals. This mass confusion, in turn, has rendered applications of classical statistical testing all but meaningless among applied researchers.
  3. Breaking the 1000ms Time to Glass Mobile Barrier (YouTube) —
    See also slides. Stay under 250 ms to feel “fast.” Stay under 1000 ms to keep users’ attention.
  4. Modern Methods for Sentiment AnalysisRecently, Google developed a method called Word2Vec that captures the context of words, while at the same time reducing the size of the data. Gentle introduction, with code.
Comment: 1
Four short links: 4 October 2013

Four short links: 4 October 2013

Neuromancer Game, Ray Ozzie, Sentiment Analysis, and Open Science Prizes

  1. Case and Molly, a Game Inspired by Neuromancer (Greg Borenstein) — On reading Neuromancer today, this dynamic feels all too familiar. We constantly navigate the tension between the physical and the digital in a state of continuous partial attention. We try to walk down the street while sending text messages or looking up GPS directions. We mix focused work with a stream of instant message and social media conversations. We dive into the sudden and remote intimacy of seeing a family member’s face appear on FaceTime or Google Hangout. “Case and Molly” uses the mechanics and aesthetics of Neuromancer’s account of cyberspace/meatspace coordination to explore this dynamic.
  2. Rethinking Ray Ozziean inescapable conclusion: Ray Ozzie was right. And Microsoft’s senior leadership did not listen, certainly not at the time, and perhaps not until it was too late. Hear, hear!
  3. Recursive Deep Models for Semantic Compositionality
    Over a Sentiment Treebank
    (PDF) — apparently it nails sentiment analysis, and will be “open sourced”. At least, according to this GigaOm piece, which also explains how it works.
  4. PLoS ASAP Award Finalists Announced — with pointers to interviews with the finalists, doing open access good work like disambiguating species names and doing open source drug discovery.
Comment: 1
Four short links: 30 September 2013

Four short links: 30 September 2013

Google Code Analysis, Deep Learning, Front-End Workflow, and SICP in JS

  1. Steve Yegge on GROK (YouTube) — The Grok Project is an internal Google initiative to simplify the navigation and querying of very large program source repositories. We have designed and implemented a language-neutral, canonical representation for source code and compiler metadata. Our data production pipeline runs compiler clusters over all Google’s code and third-party code, extracting syntactic and semantic information. The data is then indexed and served to a wide variety of clients with specialized needs. The entire ecosystem is evolving into an extensible platform that permits languages, tools, clients and build systems to interoperate in well-defined, standardized protocols.
  2. Deep Learning for Semantic AnalysisWhen trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effect of contrastive conjunctions as well as negation and its scope at various tree levels for both positive and negative phrases.
  3. Fireshell — workflow tools and framework for front-end developers.
  4. SICP.js — lots of Structure and Interpretation of Computer Programs (the canonical text for higher-order programming) ported to Javascript.
Four short links: 28 May 2013

Four short links: 28 May 2013

Geeky Primer, Visible CSS, Remote Working, and Raspberry Pi Sentiment Server

  1. My Little Geek — children’s primer with a geeky bent. A is for Android, B is for Binary, C is for Caffeine …. They have a Kickstarter for two sequels: numbers and shapes.
  2. Visible CSS RulesEnter a url to see how the css rules interact with that page.
  3. How to Work Remotely — none of this is rocket science, it’s all true and things we had to learn the hard way.
  4. Raspberry Pi Twitter Sentiment Server — step-by-step guide, and github repo for the lazy. (via Jason Bell)

The hidden language and "wonderful experience" of product reviews

Panagiotis Ipeirotis on the phrases and formatting of effective product reviews.

How much is an Amazon review — good or bad — worth? Computer scientist and NYU professor Panagiotis Ipeirotis analyzed the text in thousands of Amazon reviews to find out.

Comments: 3

Demoting Halder: A wild look at social tracking and sentiment analysis

You no longer have control over where a first impression occurs.

My short story, "Demoting Halder," was supposed to lay out an alternative reality where social tracking and sentiment analysis had taken over society. As the story evolved, I wondered if the reality in the story is something we're living right now.

Comments: 2

Visualization of the Week: Sentiment in the Bible

Sentiment analysis sheds new light on an old book.

OpenBible.info found a novel way to examine one of the world's most analyzed texts: Create a visualization showing the rise and fall of sentiment across the Bible.


Trading on sentiment

Sentiment analysis gives algorithmic trading an edge

Sorting through thousands of news stories and categorizing information based on mood and tone creates useful data points for financial systems.

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Four short links: 3 May 2011

Four short links: 3 May 2011

Sentiment Analysis, Word Frequency, Design Process, and Plant Recognition

  1. SentiWordNet — WordNet with hints as to sentiment of particular terms, for use in sentiment analysis. (via Matt Biddulph)
  2. Word Frequency Lists and Dictionaries — also for text analysis. This site contains what we believe is the most accurate frequency data of English. It contains word frequency lists of the top 60,000 words (lemmas) in English, collocates lists (looking at nearby words to see word meaning and use), and n-grams (the frequency of all two and three-word sequences in the corpora).
  3. Crash Course in Web Design for StartupsWhen I was a wee pixel pusher I would overuse whatever graphic effect I had just learned. Text-shadow? Awesome, let’s put 5px 5px 5px #444. Border-radius? Knock that up to 15px. Gradients? How about from red to black? You can imagine how horrible everything looked. Now my rule of thumb in most cases is applying just enough to make it perceivable, no more. This usually means no blur on text-shadow and just a 1px offset, or only dealing with gradients moving between a very narrow color range. Almost everything in life is improved with this rule.
  4. LeafsnapColumbia University, the University of Maryland and the Smithsonian Institution have pooled their expertise to create the world’s first plant identification mobile app using visual search—Leafsnap. This electronic field guide allows users to identify tree species simply by taking a photograph of the tree’s leaves. In addition to the species name, Leafsnap provides high-resolution photographs and information about the tree’s flowers, fruit, seeds and bark—giving the user a comprehensive understanding of the species. iPhone for now, Android and iPad to come. (via Fiona Romeo)
Comments: 2

With sentiment analysis, context always matters

Matthew Russell on the limitations and applications of sentiment analysis.

Though sentiment analysis is subjective, Matthew Russell says using transparent methods and keeping the data in context are keys to making it an effective tool.

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