uncleflo
Registered since September 28th, 2017
Has a total of 4246 bookmarks.
Showing top Tags within 13 bookmarks
howto information development guide reference administration design website software solution service product online business uk tool company linux code server system application web list video marine create data experience description tutorial explanation technology build blog article learn world project boat download windows security lookup free performance javascript technical network control beautiful support london tools course file research purchase library programming image youtube example php construction html opensource quality install community computer profile feature power browser music platform mobile user process work database share manage hardware professional buy industry internet dance advice installation developer 3d material search access customer camera travel test standard review documentation css money engineering develop webdesign engine device photography digital api speed source program management phone discussion question event client story simple water marketing yacht app content setup package fast idea interface account communication cheap compare script study market live easy google resource operation startup monitor training
Tag selected: keyword.
Looking up keyword tag. Showing 13 results. Clear
2 Sentiment analysis with tidy data | Text Mining with R
Saved by uncleflo on December 23rd, 2018.
In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. This allowed us to analyze which words are used most frequently in documents and to compare documents, but now let’s investigate a different topic. Let’s address the topic of opinion mining or sentiment analysis. When human readers approach a text, we use our understanding of the emotional intent of words to infer whether a section of text is positive or negative, or perhaps characterized by some other more nuanced emotion like surprise or disgust. We can use the tools of text mining to approach the emotional content of text programmatically, as shown in Figure 2.1.
tidy sentiment normalize toolbox sentiments analyze calculate words analysis development code ruby developer howto technical book demonstration frequency lingual language emotional approach investigate compare keyword useful web
Term Frequency and Inverse Document Frequency (tf-idf) Using Tidy Data Principles
Saved by uncleflo on December 23rd, 2018.
A central question in text mining and natural language processing is how to quantify what a document is about. Can we do this by looking at the words that make up the document? One measure of how important a word may be is its term frequency (tf), how frequently a word occurs in a document. There are words in a document, however, that occur many times but may not be important; in English, these are probably words like “the”, “is”, “of”, and so forth. We might take the approach of adding words like these to a list of stop words and removing them before analysis, but it is possible that some of these words might be more important in some documents than others. A list of stop words is not a sophisticated approach to adjusting term frequency for commonly used words. Another approach is to look at a term’s inverse document frequency (idf), which decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents. This can be combined with term frequency to calculate a term’s tf-idf, the frequency of a term adjusted for how rarely it is used. It is intended to measure how important a word is to a document in a collection (or corpus) of documents. It is a rule-of-thumb or heuristic quantity; while it has proved useful in text mining, search engines, etc., its theoretical foundations are considered less than firm by information theory experts.
quantify tidy calculate corpus document words frequency calculating verbs numerical examine occur text weight quantity approach mining collection keyword tag analyse development howto data principle useful technical analysis developer code explanation article
How can we find the tf-idf value of a word in the corpus?
Saved by uncleflo on December 23rd, 2018.
I am working on text classification using SVM. In a paper (Fuzzy Support vector machine for multi-class text categorization) the author has reduced the features(words) by applying the following criteria: "Eliminate the words that are ICF>log2, Uni<0.2 and TF_IDF<26". My question is how can we find TF_IDF value of a word. TF is a local measure and IDF is a global measure. TF_IDF gives different value for a word in each document. TF-IDF is the acronym for Term Frequency–Inverse Document Frequency. This metric aims at estimating how important is a keyword not only in a particular document, but rather in a whole collection of documents (corpus). Actually, a lot of common words like articles or conjunctions may appear several times in a document but they are not relevant as key-concepts to be indexed or searched. TF (Term Frequency) provides a measure about how frequently a term occurs in a document.
tf-idf question solution answer vector machine text categorization keyword estimate article lookup development server document
Weighting words using Tf-Idf - NLP-FOR-HACKERS
Saved by uncleflo on December 23rd, 2018.
If I ask you “Do you remember the article about electrons in NY Times?” there’s a better chance you will remember it than if I asked you “Do you remember the article about electrons in the Physics books?”. Here’s why: an article about electrons in NY Times is far less common than in a collection of physics books. It is less likely to stumble upon the “electron” concept in NY Times than in a physics book. Let’s consider now the scenario of a single article. Suppose you read an article and you’re asked to rank the concepts found in the article by importance. The chances are you’ll basically order the concepts by frequency. The reason is simply that important stuff would be mentioned repeatedly because the narrative gravitates around them. Combining the 2 insights, given a term, a document and a collection of documents we can loosely say that:importance ~ appearances(term, document) / count(documents containing term in collection).
python classifier compute implement compile calculate corpus classify phrases extraction compare advise keyword technical development howto suggestion article frequency analysis tf-idf importance administration
AtoS - Trusted partner for your Digital Journey
Saved by uncleflo on October 23rd, 2018.
As technology continuously disrupts the norms, our clients can rely on us to guide them through a successful digital transformation. At Atos, we embrace this journey, striving to remain the trusted partner that delivers digital empowerment to our clients. Our keywords are digital transformation, innovation and value creation, both for our own company and for our clients. We have cemented our position as the trusted partner for our clients’ digital transformation, with the resources, the scale and the know-how that our clients need. Our management is fully dedicated and committed to helping our Group prepare for the future and ensure value is delivered to clients, shareholders, partners and employees. As a responsible employer and an ethical business partner, Atos’ aspiration is to create shared value for all our customers and other stakeholders. We believe that digital technologies can deliver a fairer and more inclusive world and can help protect our environment for generations to come. Alliances and partnerships with other industry leaders are crucial for us to combine world-class capabilities and provide the most innovative customer-focused offerings.
trust partner digital journey atos aspiration preparation transformation company client keyword industry future cybersecurity security cyber online profile advisor relationship value create
Show Server Headers for a Webpage
Saved by uncleflo on June 30th, 2017.
Gain a Competitive Advantage Today Your top competitors have been investing into their marketing strategy for years. Now you can know exactly where they rank, pick off their best keywords, and track new opportunities as they emerge. Explore the ranking profile of your competitors in Google and Bing today using SEMrush. Enter a competing URL below to quickly gain access to their organic & paid search performance history - for free. See where they rank & beat them!
seo competition advantage competitor invest header keyword performance check checker analysis online monitor advice
61 Experts Reveal Best Tools For Keyword Research
Saved by uncleflo on July 23rd, 2016.
Keyword research is at the core of any SEO, PPC or content marketing campaign. If you aren't bidding on or using the right keywords, you're not serving the right content to the right audience at the right stage in the buyer journey. This means less traffic, leads, customers...and dollars.
keyword expert tool adword research business web recommendation competition profit online question simple trend mozilla samurai track compare list reference administration website marketer access pro feature manage analytics
Google Keyword Planner's Dirty Secrets
Saved by uncleflo on July 23rd, 2016.
Sometimes our best data sources aren't exactly up to par. While nearly every search marketer will rely on Google Keyword Planner data at one point or another, especially while doing keyword research, the reality is that the data is often untrustworthy and should be viewed with great skepticism. Whether you plan to use it to help build a paid search campaign or determine which content to write, there are huge caveats to the numbers presented as Average Search Volume. Today, I want to walk through a number of the "gotchas" in Google Keyword Planner data so you can do better keyword research and make smarter decisions for you or your clients' sites.
keyword research advanced seo search marketing paid advertisement ppc planner tool skeptical lies truth data source client site website administration marketer product moz mozilla dirty information untrue false interesting
10 Great Alternatives to the Google Keyword Research Tool
Saved by uncleflo on July 23rd, 2016.
10 best alternatives to the Google Keyword ToolIt is an indispensable tool for developing a potent inbound marketing strategy and improving SEO. Search engines favor compelling content, but you won’t generate much traffic if you don’t include the words and phrases people are most interested in and actively searching for. Because many of the available keyword research tools are confusing and counterintuitive, the process can be challenging and time-consuming.
alternative tool adword planner account marketing cost keyword research confuse intuitive process challenge traffic plrase strategy improve digital website
Free Keyword Tools
Saved by uncleflo on July 23rd, 2016.
WordStream's free keyword tools are an integrated, actionable suite designed to help search marketers with keyword suggestion, keyword grouping, keyword analysis, long-tail keyword research and negative keyword discovery. Our fast, powerful tools draw from a trillion-keyword database and go beyond the capabilities that a typical free keyword tool can offer.
free keyword tools wordstream advice development administration marketing offer ppc online advertising howto easy suggestion niche negative tool seo campaign data pocket opportunity value structure discover query suite marketer search software service agency pay per click budget
Build a Brand: A Complete Guide to Domain Names, Logos, Social Media and More
Saved by uncleflo on June 4th, 2014.
Even wanted to take an ordinary blog, or online business, and covert it into a brand? Have a knack for cooking, or travelling, and even an online presence, but don’t know where to go now? In this (long overdue) post, I’d like to show you exactly how I used simple things at our disposal, like domain names, logos, social media, missions statements, and more, to convert a standard WordPress blog into a fairly recognized name in blogging.
blog logo social media name brand business google keyword howto explanation guide define product service domain typography phrase word font audience mission wordpress statement
Spelling Typo Generator ~ Keyword Mispelling Tool
Saved by uncleflo on January 24th, 2014.
Use this tool to generate a list of typos and common misspellings for AdWords ads and domain names. This software/script should help you in the generation of a low competition keywords to save money on PPC ads or help you find low competition keywords which are easier to rank for with traditional SEO.
generator typo miss tool spell keyword domain name generate script
Alexa the Web Information Company
Saved by uncleflo on March 30th, 2012.
The Global Leader in Analytics. Alexa is the leading provider of free, global web metrics. Search Alexa to discover the most successful sites on the web by keyword, category, or country. Use our analytics for competitive analysis, benchmarking, market research, or business development. Use Alexa's Pro tools to optimize your company's presence on the web. Urls to this site for data on particular websites are www.alexa.com/siteinfo/{domainname}
domain name world statistics stat analysis benchmarking web keyword category country information graph development market research
No further bookmarks found.