This work uses SentiStrength Database with an improved algorithm for sentiment analysis in tweets. It also uses advanced pattern matching techniques with automata, weight enhancement of senti words based on preceding terms like "very" "Nice". It reverses the polarity based on negative words before senti words. Not Good is considered as bad. Refer my paper for more details: http://www.ijera.com/papers/Vol2_issue1/BM021412416.pdf
Views: 5650 rupam rupam
5th Annual Wolfram Data Summit 2014 Ronen Feldman, Chief Scientist, Digital Trowel Sentiment analysis is defined as the task of finding the opinions of authors about specific entities. The decision making process of people is affected by the opinions formed by thought leaders and ordinary people. In this talk, we mostly focus on analyzing subjective sentences. However, we refer to the usage of objective sentences when we describe a sentiment application for stock picking. For the latest information, please visit: http://www.wolfram.com
Views: 4840 Wolfram
Source Code: https://goo.gl/Q3Gt5m References: https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/ http://www.inf.ed.ac.uk/teaching/courses/inf2b/learnnotes/inf2b-learn-note07-2up.pdf https://data.world/datasets/twitter In this video I explain how you can use machine learning algorithms on text data, using the example of twitter sentiment analysis. I have got the dataset of trump related tweets. It is there in the above mentioned website. This code looks though all the data and then figures out if a tweet is a positive tweet or a negative tweet. After the classification(positive sentiment/negative sentiment) it saves the data in a file. Code work offers you a variety of educational videos to enhance your programming skills. At times I create videos without prior preparations so that I can show you the mistakes I am making so that you don't repeat those mistakes yourself. It's humanly to make errors, so if you find some errors in my videos please leave a comment below and I will address them or you can email me at [email protected] stating the problem. I shall try to address all of you . Finally please hit hike . . . and do subscribe so that you get to know at once when some video is being released. Happy coding . .. Epic pen: http://epic-pen.com Screen Recorder: https://obsproject.com/ Facebook https://www.facebook.com/Coding-algorithms-datastructure-Codeworks-1520910904866937/ google plus https://plus.google.com/118085047343771284166 My Website: http://www.the-tinker-project.co.in/blog/
Views: 4430 code works
Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 2772 SuperDataScience
We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 163432 Timothy DAuria
Views: 263 Adrien GC
This tutorial video covers how to do real-time analysis alongside your streaming Twitter API v1.1 feed. In this case, for example, we use the Sentdex Sentiment Analysis API, http://sentdex.com/sentiment-analysis-api/, though you can use ANY API like this, or just your own custom function too. If you don't already have a twitter stream set up, here is some sample code and tutorial video for it: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/ Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 70623 sentdex
Text Analytics Toolbox™ provides tools for extracting text from documents, preprocessing raw text, visualizing text, and performing machine learning on text data. The typical workflow begins by importing text data from documents, such as PDF and Microsoft® Word® files, and then extracting meaningful words from the data. Once text is preprocessed, you can interact with your data in a number of ways, including converting the text into a numeric representation and visualizing the text with word clouds or scatter plots. Features created with Text Analytics Toolbox can also be combined with features from other data sources to build machine learning models that take advantage of textual, numeric, audio, and other types of data. You can import pretrained word-embedding models, such as those available in word2vec, FastText, and GloVe formats, to map the words in your dataset to their corresponding word vectors. You can also perform topic modeling and dimensionality reduction with machine learning algorithms such as LDA and LSA. To get started transforming large sets of text data into meaningful insight, download a free trial of Text Analytics Toolbox: http://bit.ly/2Jp3t6a Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Views: 1015 MATLAB
I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 149947 Siraj Raval
In this video tutorial, Tahir Hussain Babar (Bob) shows how Tolerant stemming in SPS11 works. It is available for Dutch, English, German and Italian. This default behavior allows for handling non-standard spellings to better maximize recall. For example in English, the stemmer handles spelling variation found in American and British English. In this video, Bob shows how it works. Scripts ; https://github.com/saphanaacademy/TextAnalysis_Search_Mining/blob/master/TextAnalysis_SPS11.txt Thank you for watching. Video by the SAP HANA Academy. SOCIAL MEDIA Feel free to connect with us at the links below: LinkedIn: https://linkedin.com/saphanaacademy Twitter: https://twitter.com/saphanaacademy Facebook: https://www.facebook.com/saphanaacademy/ Google+: https://plus.google.com/u/0/111935864030551244982 Github: https://github.com/saphanaacademy
Views: 588 SAP HANA Academy
In this webinar we gave an in-depth introduction into sentiment analysis of financial markets. We analyze different sentiment indicators and hightlight the pros and cons. Then we introduce the sentix Global Investor Survey (http://www.sentix.co.uk) and explain how to use the sentix indices. The presentation as a PDF is available here: http://www.sentix.de/index.php/en/Woolly-thoughts-blog/sentix-webinar-about-sentiment-analysis-video-and-presentation.html For further information click http://www.sentix.co.uk
Views: 612 sentix GmbH
DATA MINING It is the process to discover the knowledge or hidden pattern form large databases. The overall goal of data mining is to extract and obtain information from databases and transfer it into an understandable format for use in future. It is used by Business intelligence organizations, Financial analysts, Marketing organizations, and companies with a strong consumer focus like retail ,financial and communication . DATA MINING (cont.): It can also be seen as one of the core process of knowledge discovery in data base (KDD). It can be viewed as process of Knowledge Discovery in database. Data Extraction/gathering:- To collect the data from sources . Eg: data warehousing. Data cleansing :- To eliminate bogus data and errors. Feature extraction:- To extract only task relevant data : i.e to obtain the interesting attributes of data . Pattern extraction and discovery :- This step is seen as process of data mining , where one should concentrate the effort. Visualization of the data and Evaluation of results :- To create knowledge base. CLASSIFICATION Classification is a technique of data mining to classify each item into predefined set of groups or classes. The goal of classification is to accurately predict the target class for each item in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating. SENTIMENT ANALYSIS Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be his or her judgment or evaluation, affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader). With opinion mining, we can distinguish poor content from high quality content. For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Views: 113 E2MATRIX RESEARCH LAB
Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1782 Quantopian
Check out this demonstration of IBM SPSS Text Analytics for Surveys to help you get up and running quickly with your free trial. Learn more about IBM SPSS http://ibm.co/spsstrial Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: http://www.ibmbigdatahub.com https://www.youtube.com/user/ibmbigdata https://www.facebook.com/IBManalytics https://www.twitter.com/IBMAnalytics https://www.linkedin.com/company/ibm-big-data-&-analytics https://www.slideshare.net/IBMBDA
Views: 10195 IBM Analytics
Lexalytics provides enterprise text mining (aka text analytics) and sentiment analysis solutions. Salience is an easy to integrate engine that will structure millions of tweets, emails, comments, surveys or any other textual data in a matter of minutes. A text analysis from Salience will extract sentiment bearing phrases, entities and themes from the text. It can also automatically categorize text using a queries - a Boolean login based classifier, or user categories - a classifier based on the semantic knowledge from wikipedia. Head to Lexalytics' website to try a free demo: http://www.lexalytics.com/web-demo
Views: 5857 Lexalytics
Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 4534 Paula Guilfoyle
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Classifying tweets http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3902 WekaMOOC
Mehr Infos im Blog: https://www.maibornwolff.de/blog/aspekt-basierte-sentiment-analyse Die Folien des Vortrags gibt es hier zum Download: http://download.maibornwolff.de/Joint_Aspect_and_Sentiment_Analysis.pdf
Views: 48 MaibornWolff GmbH
This is a example in GATE which shows the results of the default ANNIE pipeline on an English document. In this case the document is "That's what she said" that lovely catch phrase from Michael Scott in The Office TV show http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.pdf it discusses humor recognition...
Views: 29813 cesine0
This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 66222 deltaDNA
SAS Technical Consultant Jenn Sykes about her use of SAS Sentiment Analysis and SAS Forecast Studio to predict outcomes of popular elections. To learn more , read the paper "Predicting Electoral Outcomes with SAS ® Sentiment Analysis and SAS ® Forecast Studio " at http://support.sas.com/resources/papers/proceedings12/131-2012.pdf To learn more about SAS Sentiment Analysis, visit http://www.sas.com/text-analytics/sentiment-analysis/
Views: 1197 SAS Software
This is a demonstration based session which will show how to use a HDInsight (Apache Hadoop exposed as an Azure Service) cluster to do sentiment analysis from live Twitter feeds on a specific keyword or brand. Sentiment analysis is parsing unstructured data that represents opinions, emotions, and attitudes contained in sources such as social media posts, blogs, online product reviews, and customer support interactions. The demo uses Hadoop Hive and MapReduce to schematize, refine and transform raw Twitter data. It will also focuses on the Hive endpoint that HDInsight exposes for client applications to consume HDInsight data through the Hive ODBC interface. Finally, this session will show the present day self-service BI tools (Power View, Power Query and Power Map) to demonstrate how you can generate powerful and interactive visualization on your twitter data to enhance your brand promotion/productivity with just a few mouse clicks.
Views: 35334 Debarchan Sarkar
Paper Link: https://www.comp.nus.edu.sg/~leews/publications/acl17.pdf . . Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
Views: 344 Artificial Intelligence - All in One
This example takes a Course syllabus (mostly semantics courses) and highlights the reading lists using Jape grammars. It recognizes things like Van Fintel and Heim 2003 as a citation and Chapters 1, 3 and 8 as a reading selections and Week 1 as a due date (among others). Its another example of what GATE can do, in this case to help automate tasks like downloading a reading list. The files are in here https://github.com/cesine/GATEinSpring/tree/master/gate/WEB-INF/gate-files
Views: 12494 cesine0
Originally presented at AllFacebook Marketing Conference (a Mediabistro event) in San Francisco, CA on June 5, 2013. Jay Budzik, Chief Technology Officer at Perfect Market, and Jason Jedlinski, VP of Digital Products & Platforms at Tribune Broadcasting, shared how Perfect Social, Perfect Market's social sharing tool, helped KTLA.com nearly double its Facebook referral traffic. Perfect Social is available to premium digital publishers (10M+ PVs per month) for a 60-day free trial: http://goo.gl/dSaJ8
Views: 722 ThePerfectMarket
This 7:20 video introduces the viewer to the software tool known as LIWC ('Luke'), aka the Linguistic Inquiry and Word Count program. This video is supported by the Centre for Human Evolution, Cognition and Culture, and its Cultural Evolution of Religion project, at University of British Columbia. The HECC website has accompanying instructional blog posts about the use of LIWC here http://www.hecc.ubc.ca/cerc/.
Views: 8783 ryantatenichols
Sentiment Analysis with the R programming language ! Please Subscribe ! ►Websites: http://everythingcomputerscience.com/ ►C-Programming Tutorial: https://www.udemy.com/c-programming-for-complete-beginners/learn/v4/overview ►Become a Patreot: https://www.patreon.com/randerson112358 ►PROGRAMMING BOOKS C-Programming - https://www.amazon.com/gp/product/0131103628/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0131103628&linkId=764c7627ffb13944091b2ad15fb5de90 Head First Java - https://www.amazon.com/gp/product/0596009208/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0596009208&linkId=58082f233879197beb1aeb73b03c1ed8 ►DISCRETE STRUCTURES/MATHEMATICS BOOKS Discrete Mathematics Workbook- https://www.amazon.com/gp/product/0130463272/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130463272&linkId=83220d3b9eb58fb0566fa51c0e5b5571 Practice Problems in Discrete Mathematics -https://www.amazon.com/gp/product/0130458031/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130458031&linkId=e6c98555ea0342d902afda0221a1a8fb ►ALGORITHMS BOOKS Algorithm Analysis - https://www.amazon.com/gp/product/0262033844/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0262033844&linkId=ba3b1d4075fbd043bb4596a0df9402e9 Resource: https://cran.r-project.org/web/packages/RSentiment/RSentiment.pdf Get the code here: https://github.com/randerson112358/R-Programs/blob/master/Sentiment%20Analysis/Sentiment_Analysis.r
Views: 167 Computer Science
Information extraction brought to you by Text Mining Solutions we explain the process of text mining in 3 easy to understand steps. 1. Organise your input documents. 2. Processing your documents. 3. Analyse your results. This video is perfect for anyone new to Text Mining. For more information go to http://www.textminingsolutions.co.uk Follow Text Mining Solutions on: Facebook: https://www.facebook.com/TextMiningSolutions?fref=ts Twitter: https://twitter.com/Txt_Mining LinkedIn: https://www.linkedin.com/company/text-mining-solutions Music: http://www.purple-planet.com
Views: 490 TxtMining
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 25 Clickmyproject
This video tutorial has been taken from Text Mining with Machine Learning and Python. You can learn more and buy the full video course here [http://bit.ly/2IKNwe0] Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 209 Packt Video
Graph-based Analysis and Prediction for Software Evolution Pamela Bhattacharya, Marios Iliofotou, Iulian Neamtiu, and Michalis Faloutsos. International Conference on Software Engineering (ICSE 2012), June 2012. http://www.cs.ucr.edu/~neamtiu/pubs/icse12bhattacharya.pdf
Views: 433 UCR RIPLE
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Views: 1782 Kung Fu Nerd
Exaptive uses natural language processing to identify higher level topics within raw text documents. In this video, we take a look at how the process leads to explorable results. TRANSCRIPT: Exaptive makes it easier to extract meaningful insight out of raw text. In some cases Exaptive makes it possible when the barrier was too high before. Sources change quickly. So do the questions you want to ask. And not every question can be anticipated ahead of time. The best way to get insight from raw text is to be able to explore and pivot quickly. In this example, we used raw text from online discussion forums. This chart represents one forum over time. The analyst first drills down by selecting a geographic area. Then selecting communications over a certain period of time after seeing a quantitative view of how much activity is taking place. By building a network diagram, the analyst can see who is talking, and who is listening. And then the analyst can view different periods of time during the discussion to see how the network evolves. The topics of conversations can also be added to the network. The topics are an aggregation of the raw text from the discussion forums, showing who is talking about what and when. A different visualization can provide a view of how these topics occur, and recur, over time. In this example, voting became a topic September through November. And crime was a recurring topic in the winter and at the height of summer. The category - the word ‘crime’ - was not an explicit topic of conversation. The raw text - the words used - were processed and aggregated into those topic categories to draw attention to the raw data most relevant to the analysis. Findings can then be easily shared for further collaboration. In this example, it would be via PDF. All these components, whether visual or behind the scenes, are independent, reusable components in the Exaptive platform that can be leveraged with different data to provide the insight you need, as you need it. www.exaptive.com
Views: 38 Exaptive
More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Multinomial Naïve Bayes http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 19299 WekaMOOC
Full paper at https://www.cs.colorado.edu/~jbg/docs/2015_acl_dan.pdf
Views: 420 Jordan Boyd-Graber