essays and ideas from Eliza Sarobhasa
Python NLTK Sentiment Analysis with Text Classification Demo
This is a demonstration of sentiment analysis using a NLTK 2.0.4 powered text classification process. It can tell you whether it thinks the text you enter below expresses positive sentiment, negative sentiment, or if it’s neutral.
Using hierarchical classification, neutrality is determined first, and sentiment polarity is determined second, but only if the text is not neutral.
Source: I don’t know if there’s a uniform Mozilla position on this, but here’s mine! 🙂 … | Hacker News
I don’t know if there’s a uniform Mozilla position on this, but here’s mine! 🙂 The main reason I care about the Web is because it’s the world’s biggest software platform that isn’t owned. If someone can deliver their app to the world without submitting it for review by an app store and without paying a company a %-age of the revenue, and if they can market it through the viral power of URLs, then they have a lot more control over their own destiny. That’s why I think it’s important for the Web not to give up on hard but solvable problems.
But also I think there’s a false dichotomy between “the Web should just be for documents” and “the Web should just be for apps.” The Web is simultaneously an application platform that blows all other platforms out of the water for delivering content. First, there’s a reason why so many native apps embed WebViews — despite its warts, CSS is the result of hundreds of person-years of tuning for deploying portable textual content.
But more importantly, you just can’t beat the URL. How many more times will we convince the entirety of humanity to know how to visually parse “www.zombo.com” on a billboard or in a text message? It’s easy to take the Web for granted, it’s fun to snark about its warts, and there’s a cottage industry of premature declarations of its death. But I personally believe that the humble little hyperlink is at the heart of the Web’s power, competitive strength, and longevity. It was a century-old dream passed on from Vannevar Bush to Doug Englebart to Xerox PARC and ultimately to TBL who made it real.
Figure 1. Overview of the machine intelligence landscape going into 2016. Image courtesy of Shivon Zilis.
Source: The current state of machine intelligence 2.0 – O’Reilly Media
Google Tests Feature That Lets Media Companies, Marketers Publish Directly to Search Results – WSJ
Google is experimenting with letting big publishers publish directly into search results, instead of going through SEO and indexing.
Google is experimenting with a new feature that allows marketers, media companies, politicians and other organizations to publish content directly to Google and have it appear instantly in search results.
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Google has built a Web-based interface through which posts can be formatted and uploaded directly to its systems. The posts can be up to 14,400 characters in length and can include links and up to 10 images or videos. The pages also include options to share them via Twitter, Facebook or email.
Each post is hosted by Google itself on a dedicated page, and appears in a carousel in results pages for searches related to their authors for up to a week, a Google spokeswoman said. After seven days, the posts remain live but won’t be surfaced in search results. Rather, they can be accessed via a link.
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The Google spokeswoman said the experimental feature is separate from Google’s Accelerated Mobile Pages program, which aims to speed up online content by streamlining the code that powers Web pages.
With the AMP program, Google also “caches” pages, or saves copies of them on its own systems, in order to deliver them more quickly to users. AMP doesn’t host content directly, however, whereas Google’s new search feature does.
Google’s tests of the new posting tool comes at a time when media companies, marketers and organizations of all types are increasingly distributing content by publishing directly to major online platforms, instead of driving users back to their own websites and properties.
Facebook has an Instant Articles feature, for example, which lets anyone host their content directly on the social network, provided they adhere to its content policies. Facebook also overhauled its own “Notes” feature in September 2015 which—similarly to Google’s new feature—offers a Web interface through which users can publish their content directly to the social network.
Apple also unveiled a Web-based publishing tool that allows users to arrange and publish content directly to its Apple News application.
The Google spokeswoman emphasized that the new tool remains in an experimental phase, and wouldn’t provide details on if or when it may be opened up to more authors. Google is currently testing it with a range of different type of partners, she said, but wouldn’t disclose exactly how many.
1951 gave us Interlingua and Simpson's paradox. pic.twitter.com/HJdLSCJYIB
— Object Lessons (@objectsobjects) April 30, 2016
The current state of machine intelligence 2.0 – O’Reilly Media
A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since.
I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive.
Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways. Machine intelligence already helps us get the important business information we need more quickly, monitors critical systems, feeds our population more efficiently, reduces the cost of health care, detects disease earlier, and so on.
The two biggest changes I’ve noted since I did this analysis last year are
(1) the emergence of autonomous systems in both the physical and virtual world
and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.
Intro To Computational Linguistics
ELIZA
Natural language processing comes in many varieties. The most robust natural language systems are tailored to the most limited applications. The simplest approach to natural language processing is to program the computer to look for a limited set of key words or phrases. When the computer finds these words it produces a programmed response. The ELIZA program offers a particularly compelling example of the keyword approach to natural language processing. ELIZA was written at MIT in the mid-1960s to mimic the role of a psychoanalyst interviewing a patient. Examples of ELIZA and related programs are now widely available on the web and personal computers.
ELIZA was never intended to be a model of natural language understanding, yet it is still one of the most popular artificial intelligence programs in the public domain. As long as the user accepts the premise that the program is conducting an open-ended interview, ELIZA can produce a convincing imitation of a talking computer. ELIZA works by searching for a list of keywords in the input. If the program finds one of these words, it asks a preprogrammed question that centers around the keyword. If the program does not find a word on its list, it chooses from a set of open-ended responses, such as Tell me more or Go on. Continue reading “Intro To Computational Linguistics”