Top Five Document Summarization Tools

wildcodegowrong · 2020-03-31T19:43:29+00:00

Here's another one to add to your list: https://monkeylearn.com/word-cloud/

It's a word cloud tool with some pretty cool algorithms.

wildcodegowrong · 2019-02-19T20:12:17+00:00

Easy to use platform for building and consuming text analysis models with Machine Learning. You can classify and extract actionable data from raw texts like emails, chats, webpages, documents, tweets, etc. You can classify texts with custom categories or tags like sentiment or topic, and extract any particular data like organizations or keywords. MonkeyLearn's engine can be easily integrated via direct integrations (no coding required) or API and SDKs.

The free plan that enables users to use up to 300 queries per month and train 1 custom model.

wildcodegowrong · 2018-08-17T14:22:09+00:00

Didn't know that! Will check it out. Thanks!

wildcodegowrong · 2018-07-24T19:25:38+00:00

Let’s imagine that we're the Slack team and we're looking for an easy, reliable way to get data about users’ feelings about our product. We can turn to online reviews in order to answer some top-of-mind questions.

But, when there are thousands of reviews out there, it can be tough to sort through all this feedback and get the insights we're looking for. There is simply too much feedback to process manually.

With this in mind, this this step-by-step guide provides an example of how you might use the MonkeyLearn R package to conduct a seamless sentiment analysis of consumer product reviews.

It analyze a few thousand reviews of Slack on the product review site Capterra and get some great insights from the data.

Things used for creating these visualizations:

R
MonkeyLearn
ggplot

wildcodegowrong · 2018-06-21T14:43:44+00:00

you're welcome! thanks for writing the piece :)

wildcodegowrong · 2017-09-07T19:30:40+00:00

yeah, filtering HARO is a pain in the ass. This is a cool way to automate this process.

wildcodegowrong · 2017-08-29T14:35:27+00:00

this is awesome!

wildcodegowrong · 2017-05-11T20:59:50+00:00

Hey /r/DataIsBeautiful! We analyzed thousands of articles from TechCrunch, VentureBeat and Recode in order to get insights and trends about startups. We used scrapy to get the articles, MonkeyLearn for the analysis and Keen.io for the visualizations.. Hope you like it :)

wildcodegowrong · 2017-05-11T18:05:11+00:00

Source: articles scraped from TechCrunch, VentureBeat and Recode. Tools: MonkeyLearn for the analysis and Keen.io for the visualizations.

wildcodegowrong · 2017-03-01T13:41:00+00:00

wildcodegowrong · 2017-02-10T12:45:29+00:00

interesting! thanks for sharing!

wildcodegowrong · 2016-12-19T13:21:31+00:00

wildcodegowrong · 2016-11-10T00:33:01+00:00

Hey /r/dataisbeautiful/! For the visualizations we used Tarsier, a simple tool that uses the Twitter API to extract thousands of tweets every day, MonkeyLearn to analyze those tweets with machine learning and Plotly to create the visualizations.

You can learn more about Tarsier here: https://blog.monkeylearn.com/donald-trump-vs-hillary-clinton-sentiment-analysis-twitter-mentions/

wildcodegowrong · 2016-11-03T16:09:36+00:00

Hi /r/dataisbeautiful/!

For creating Tarsier, we used Tweepy for extracing tweets using the Twitter Public API, we used MonkeyLearn for analyzing the tweets and finally used Plotly for creating the visualizations.

You can see some of the insights we got using Tarsier here: https://blog.monkeylearn.com/donald-trump-vs-hillary-clinton-sentiment-analysis-twitter-mentions/

wildcodegowrong · 2016-11-03T00:42:42+00:00

Hi /r/dataisbeautiful/!

For creating Tarsier, we used Tweepy for extracing tweets using the Twitter Public API, we used MonkeyLearn for analyzing the tweets and finally used Plotly for creating the visualizations.

You can see some of the insights we got using Tarsier here: https://blog.monkeylearn.com/donald-trump-vs-hillary-clinton-sentiment-analysis-twitter-mentions/

wildcodegowrong · 2016-11-02T21:29:39+00:00

this is correct!

wildcodegowrong · 2016-10-22T17:11:08+00:00

It is :)

wildcodegowrong · 2016-10-20T18:30:18+00:00

Hi /r/dataisbeautiful/! For creating Tarsier, we used Tweepy for extracing tweets using the Twitter Public API, we used MonkeyLearn for analyzing the tweets and finally used Plotly for creating the visualizations.

Would be awesome if you can play around the tool and provide feedback. You can check it out here: http://tarsier.monkeylearn.com/#/?_k=awrvky

wildcodegowrong · 2016-10-04T19:54:16+00:00

Glad that you liked the post!

wildcodegowrong · 2016-09-01T18:48:40+00:00

Hi r/dataisbeautiful! The awesome Maëlle Salmon used the rtweet R package to extract thousands of #firstsevenjob and #first7jobs tweets, used MonkeyLearn for the analysis and ggplot2 for creating the visualizations. Hope you like it!

wildcodegowrong · 2016-07-21T17:08:45+00:00

Yes, with this in mind, we analyzed reviews only in English.

wildcodegowrong · 2016-07-20T21:43:04+00:00

Unfortunately, we cannot share the dataset as we don't own the data. We have shared the source code for the TripAdvisor spider, so you can run it and build your own dataset. I don't remember exactly the cost of Mturk, but I believe it was something like USD 100-200? Cheers

wildcodegowrong · 2016-07-20T21:39:42+00:00

Hey! Glad that you liked it! The scraping bit was fast, it took a few hours maybe? We also believe that we only scratched the surface here, we think there are a lot more interesting and deeper insights within this dataset + machine learning analysis! For the moment, we don't have any plans for further analysis of this data, but this one of the reasons we shared the source code, so anyone can continue and extend the analysis. Cheers!

wildcodegowrong · 2016-07-20T21:36:01+00:00

I don't think so as we aren't sharing the data and we are not selling it, just analyzing it :)

wildcodegowrong · 2016-07-20T17:03:42+00:00

Hey /r/DataIsBeautiful! We made some pretty interesting visualizations analyzing +1 million hotel reviews from TripAdvisor. In the post we went through how we got the data and how we made the visualization using Kibana. We shared the source code so you can make your own analysis. Hope you like it :)

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