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Random Forest Classifier for News Articles Sentiment Analysis
 
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Introduction 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 . 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. Random Forest Technique In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample. This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
Twitter Sentiment Analysis using Hadoop on Windows
 
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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: 34662 Debarchan Sarkar
Outlining sequence and format
 
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This short video discusses outlining an essay or term paper as both sequence and format.
Views: 472 James Patterson
Academic Writing 101: Lecture 24 - Opinion Editorials
 
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Exclusive YouTube 50% off coupon to join the full course: https://www.udemy.com/academic-writing-101/?couponCode=U2BE.
Views: 1169 M Taylor
Qualitative Content Analysis
 
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A quick example of how to conduct content analysis
Views: 7754 Robin Kay
My Text Analysis Presentation!
 
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This is for my English 11 class. Feel free to watch!
Views: 81 Hannah Borst
The Hazards of AI: Beware! | Hamidreza Keshavarz Mohammadian | TEDxTehran
 
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AI is improving every day and we found a widespread application of it in our daily lives.How deep is this influence? We did get into top gear but do we have a destination or we are going nowhere? Is this beautiful forest road going to the valley? Hamidreza Keshavarz was born in Tehran in 1983. He attended the Allameh Helli school (NODET) where he later became a teacher and head of the department. He holds a Ph.D. degree in Computer Engineering from Tarbiat Modares University. His main interest areas are data science and artificial intelligence and his thesis, entitled “Sentiment analysis based on the extraction of lexicon features” is about opinion mining on social media. He has published 12 papers and is a reviewer for international journals and conferences. He was awarded for presenting his thesis in the countrywide “Presenting your thesis in three minutes” competition. He has been in love with computers since early childhood when computers were not as widespread as today. His love with computers was intensified when he started programming at age 11. He wrote a Paintbrush program in the Assembly language at age 12, which cemented his desire to become active in this field. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 383 TEDx Talks
Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *it surprises you; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. 3.10. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
Views: 666281 Kent Löfgren
Data Mining Project Proposal Example | Data Mining Thesis Proposal Example
 
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Contact Best Matlab Code Projects Visit us: http://matlab-code.org/
Views: 75 MATLAB PROJECTS
SAP Twitter Analysis App
 
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With SAP HANA, we have developed a Twitter Analysis App to address and analyse some of the ‘unstructured data’ we receive from everyday tweets. So let’s see how it works.
Views: 2155 SAP Business One
How to easily perform text data content analysis with Excel
 
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Perform complex text analysis with ease. Automatically find unique phrase patterns within text, identify phrase and word frequency, custom latent variable frequency and definition, unique and common words within text phrases, and more. This is data mining made easy. Video Topics: 1) How to insert text content data for analysis 2) Perform qualitative content analysis on sample survey 3) Review text content phrase themes and findings within data 4) Review frequency of words and phrase patterns found within data 5) Label word and phrase patterns found within data
Views: 58770 etableutilities
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 73888 Linguamatics
Finding Main Ideas and Supporting Details Example
 
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A simple explanation and example of finding the main idea and supporting details in a paragraph.
Views: 125910 ProgressiveBridges
MLSA - Multi Language Sentiment Analysis
 
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JHU Information Retrieval class project. Performing sentiment analysis on ranked documents retrieved per user query on multiple languages.
Views: 41 Jorge M Ramirez
My Master Thesis Presentation and Defense
 
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The presentation was made using "Keynote"
Views: 221801 Adham Elshahabi
Using twitter to predict heart disease | Lyle Ungar | TEDxPenn
 
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Can Twitter predict heart disease? Day in and day out, we use social media, making it the center of our social lives, work lives, and private lives. Lyle Ungar reveals how our behavior on social media actually reflects aspects about our health and happiness. Lyle Ungar is a professor of Computer and Information Science and Psychology at the University of Pennsylvania and has analyzed 148 million tweets from more than 1,300 counties that represent 88 percent of the U.S. population. His published research has been focused around the area of text mining. He has published over 200 articles and holds eleven patents. His current research deals with statistical natural language processing, spectral methods, and the use of social media to understand the psychology of individuals and communities. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 3816 TEDx Talks
How to use WEKA software for data mining tasks
 
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In this video i'll guide you how to use WEKA software for preprocessing, classifying, clustering, association. WEKA is a collection of machine learning algorithms for performing data mining tasks. Get WEKA from here : http://www.cs.waikato.ac.nz/ml/weka/
Views: 14393 Ranji Raj
Textual Analysis PowerPoint
 
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The overview and findings of my textual analysis project for COM610.
Views: 418 Nikki Edmondson
knitr project, thesis, article - tutorial 2
 
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Continuation of the tutorial on how to break up your projects into manageable files of R and LaTeX code. knitr makes it easy to preview each of your child documents without having to include all the preamble of "documentclass, begin/end{document}", etc.
Views: 1503 14mech14
dissertation and thesis writing| MATLAB lecture 2
 
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THESIS WORK OMN MATLAB| THESIS IN CHANDIGARH| M TECH THESIS| PHD THESIS IN CHANDIGARH This is my secondlecture on MATLAB . Please like, subscribe and share my video.Assignment Statement "=" means, Equal sign has to store its right side value into its left side variable. https://thesisworkchd.files.wordpress.com/2018/01/matlab2.pdf THESIS WORK ON MATLAB We provide thesis assistance and guidance in Chandigarh with full thesis help and readymade M.Tech thesis writing in MATLAB with full documentation in Chandigarh , Delhi, Haryana, Punjab, Jalandhar, Mohali, Panchkula, Ludiana, Amritsar and nearby area’s M.Tech. students by providing a platform for knowledge sharing between our expert team. Some of the important areas in which we provide thesis assistance presently have been listed below: BIOMEDICAL BASED PROJECTS: 1. AUTOMATIC DETECTION OF GLAUCOMA IN FUNDUS IMAGES. 2. DETECTION OF BRAIN TUMOR USING MATLAB 3. LUNG CANCER DIAGNOSIS MODEL USING BNI. 4. ELECTROCARDIOGRAM (ECG) SIMULATIONN USNG MATLAB FACE RECOGNITION: 5. FACE DETECTION USING GABOR FEATURE EXTRACTION & NEURAL NETWORK 6. FACE RECOGNITION HISTOGRAM PROCESSED GUI 7. FACE RECOGNITION USING KEKRE TRANSFORM FINGERPRINT RECOGNITION: 8. MINUTIAE BASED FINGERPRINT RECOGNITION. 9. FINGERPRINT RECOGNITION USING NEURAL NETWORK RECOGNITION/ EXTRACTION/ SEGMENTATION/WATERMARKING: 10. ENGLISH CHARACTER RECOGNITION USING NEURAL NETWORK 11. NUMBER RECOGNITION USING IMAGE PROCESSING 12. CHECK NUMBER READER USING IMAGE PROCESSING 13. DETECTION OF COLOUR OF VEHICLES. 14. SEGMENTATION & EXTRACTION OF IMAGES, TEXTS, NUMBERS, OBJECTS. 15. SHAPE RECOGNITION USING MATLAB IN THE CONTEXT OF IMAGE PROCESSING 16. RETINAL BLOOD VESSEL EXTRACTION USING MATLAB 17. RECONGITION AND LOCATING A TARGET FROM A GIVEN IMAGE. 18. PHASE BASED TEMPLATE MATCHING 19. A DETECTION OF COLOUR FROM AN INPUT IMAGE 20. CAESAR CIPHER ENCRYPTION-DECRIPTION 21. IMAGE SEGMENTATION - MULTISCALE ENERGY-BASED LEVEL SETS 22. THE IMAGE MEASUREMENT TOOL USING MATLAB 23. A DIGITAL VIDEO WATERMARKING TECHNIQUE BASED ON IDENTICAL FRAME EXTRACTION IN 3-LEVEL DWT (ALSO FOR 5-LEVEL DWT) 25. RELATED TO STEGANOGRAPHY AND CRYPTOGRAPHY 26. RELATED TO THE ALL TYPES OF WATERMARKING TECHNIQUES A. TEXT WATERMARKING B. IMAGE WATERMARKING C. VIDEO WATERMARKING D. COMBINATION OF TEXT AND IMAGE WITH KEY 27. OFFLINE SIGNATURE RECOGNITION USING NEURAL NETWORKS APPROACH 28. FRUIT RECOGNITAION RELATED PROJECTS 29. VESSEL SEGMENTATION AND TRACKING 30. PROPOSED SYSTEM FOR DATA HIDING USING CRYPTOGRAPHY AND STEGANOGRAPHY 31. BASED ON IMAGE COMPRESSION ALGORITHM USING DIFFERENT TECHNIQUES 32. GRAYSCALE IMAGE DIGITAL WATERMARKING TECHNOLOGY BASED ON WAVELET ANALYSIS 33. CONTENT-BASED IMAGE RETRIEVAL 34. IMAGE PROCESSING BASED INTELLIGENT TRAFFIC CONTROLLER 35. MORPHOLOGY APPROACH IN IMAGE PROCESSING And many more……….. http://www.thesisworkchd.com/
Views: 65 Pushpraj Kaushik
Using Excel to Find Systematic Review Patterns
 
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Use Excel to find patterns for a systematic review
Views: 9240 Scott Parrott
Evaluating Texts for Academic Reading
 
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Academic Reading
Views: 494 joseph raj
Implications of word use in outline conversation Phd dissertation of Staphan Ludwig
 
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The business implications of word use in outline conversation - This study offers suggestions for managers in managing online user – communities by assessing the online community through text – mining and specific linguistic styles.
Find themes and analyze text in NVivo 9 | NVivo Tutorial Video
 
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Learn how to use NVivo's text analysis features to help you identify themes and explore the use of language in your project. For more information about NVivo visit: http://bit.ly/sQbS3m
Views: 99691 NVivo by QSR
The Research Proposal
 
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Postgraduate students embarking on a research project are usually required to submit a Research Proposal before they can start. This Video Lecture covers the most important aspects of a Research Proposal which potential researchers need to know.
Views: 339909 Massey University
Downloading and installing MAXQDA
 
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To join online SPSS Foundation Training at just $10 click here: https://www.udemy.com/spss-statistics... Syllabus: Unit 1: Developing the familiarity with SPSS Processer Entering data in SPSS editor. Solving the compatibility issues with different types of file. Inserting and defining variables and cases. Managing fonts and labels. Data screening and cleaning. Missing Value Analysis. Sorting, Transposing, Restructuring, Splitting, and Merging. Compute & Recode functions. Visual Binning & Optimal Binning. Research with SPSS (random number generation). Unit 2: Working with descriptive statistics Frequency tables, Using frequency tables for analyzing qualitative data, Explore, Graphical representation of statistical data: histogram (simple vs. clustered), boxplot, line charts, scattorplot (simple, grouped, matrix, drop-line), P-P plots, Q-Q plots, Addressing conditionalities and errors, computing standard scores using SPSS, reporting the descriptive output in APA format. Unit 3: Hypothesis Testing Sample & Population, concept of confidence interval, Testing normality assumption in SPSS, Testing for Skewness and Kurtosis, Kolmogorov–Smirnov test, Test for outliers: Mahalanobis Test, Dealing with the non-normal data, testing for homoscedasticity (Levene’s test) and multicollinearity. Unit 4: Testing the differences between group means t – test (one sample, independent- sample, paired sample), ANOVA-GLM 1 (one way), Post-hoc analysis, Reporting the output in APA format. Unit 5: Correlational Analysis Data entry for correlational analysis, Choice of a suitable correlational coefficient: non-parametric correlation (Kendall’s tau), Parametric correlation (Pearson’s, Spearman’s), Special correlation (Biserial, Point-biserial), Partial and Distance Correlation Unit 6: Regression (Linear & Multiple) The method of Least Squares, Linear modeling, Assessing the goodness of fit, Simple regression, Multiple regression (sum of squares, R and R2 , hierarchical, step-wise), Choosing a method based on your research objectives, checking the accuracy of regression model. Unit 7: Logistic regression, Choosing method (Enter, forward, backward) & covariates, choosing contrast and reference (indicator, Helmert and others), predicted values: probabilities & group membership, Influence statistics: Cook, Leverage values, DfBetas, Residuals (unstandardized, logit, studentized, standardized, devaince), Statics and plot: classification, Hosmer-Lemeshow goodness-of-fit, performing bootstrap, Choosing the right block, interpreting -2loglikelihood, Omnibus test, interpreting contingence and classification table, interpreting Wald statistics and odd ratios. Reporting the output in APA format Unit 8: Non-parametric tests When to use, Assumptions, Comparing two independent conditions (Wilcoxon rank-sum test, Mann-Whitney test), Several independent groups (Kruskal- Wallis test), Comparing two related conditions (Wilcoxon signed-rank test), Several related groups (Friedman’s anova), Post-hoc analysis in non-parametric analysis. Categorical testing: Pearson’s Chi-square test, Fisher’s exact test, Likelihood ratio, Yates’ correction, Loglinear Analysis. Reporting the output in APA format. Unit 9: Factor Analysis Theoretical foundations of factor analysis, Exploratory and Confirmatory factor analysis, testing data sufficiency for EFA & CFA, Principal component Analysis, Factor rotation, factor extraction, using factor analysis for test construction, Interpreting the SPSS output: KMO & Bartlett’s test, initial solutions, correlation matrix, anti-image, explaining the total variance, communalities, eigen-values, scree plot, rotated component matrix, component transformation matrix, factor naming Unit 10: Structural Equation Modelling using IBM AMOS Getting familiar with AMOS graphics, defining the variables-endogeneous, exogeneous, residual; Model building, Meeting the assumptions of SEM, Dealing with the non-normal data, Bootstrapping, detecting the outliers-Mahalanobis Distance Mediation Analysis, Indirect and Direct Effects, Testing the EFA model for surveys and tests, Explaining the model-p values, estimates, standard error, critical ratio, Understanding the indices of model fit- chi square, relative chi square, GFI, AGFI, PGFI, SRMR, NFI, TLI, CFI, RMSEA; Registration: For registration in classroom course and any other details contact [email protected] Program date: Summer batch: Second week of May Winter Batch: Second week of December Fee for classroom course/person: $100 if registered 3 months advance for both batch else $ 250. Group discount available.
Views: 206 Heurexler Research
Using textmining to spot innovation in biomedical sciences
 
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What is the real novelty of a research paper? How do different researchers contribute to innovation? And does this change throughout their career? Shubhanshu Mishra of the University of Illionois uses textmining techniques to study the novelty of biomedical articles.
Views: 254 OpenMinTeD
The Weeknd - D.D.
 
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http://theweeknd.co/BeautyBehindTheMadness THE MADNESS FALL TOUR 2015: http://republicrec.co/BBTMtickets
Views: 4723573 The Weeknd
Introductory Tutorial to Chorus: A Twitter Data Collection and Analytics Suite for Social Science
 
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[Best viewed in 1080p quality] An introductory tutorial to the Chorus software suite, which provides in-depth and sophisticated social media (Twitter) data collection and analytics for social science research in academia, industry, policy and beyond. Interested viewers are very welcome to contact us to arrange access to our Chorus Desktop package, feauring full versions of Chorus-TCD and our analytics suite, Chorus-TV (TweetVis). Please engage with us on our social media accounts or contact us via email if you wish to arrange access to Chorus, to know more and/or be kept abreast of the latest updates. Contact us at: [email protected] Follow us for news and updates at: http://chorusanalytics.co.uk twitter.com/Chorus_Team [The development of Chorus was supported in part through the MATCH Programme (UK Engineering and Physical Sciences Research Council grants numbers GR/S29874/01, EP/F063822/1 and EP/G012393/1)]
Views: 7337 TheChorusTeam
Literature Review Preparation Creating a Summary Table
 
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This video shows you exactly how to create a summary table for your research articles. It outlines what information should go in the table and provides helpful summary hints. eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" on Amazon: http://amzn.to/1hB2eBd OR the PDF: http://books.google.ca/books/about/Research_terminology_simplified.html?id=tLMRAgAAQBAJ&redir_esc=y http://youstudynursing.com/ Once you have found literature that you want to include in your review the task of summarizing it can be daunting. It is helpful to use a data extraction tool while you are reviewing each article. Then, creating a table that captures key points you need to consider for your analysis will make your summary more accurate, effective and complete. This step is so important that I get my students to do it for marks. If you are working on a literature review, trust me, you don't want to skip this step. If you do, the review will end up taking a lot longer to complete and you will be more likely to miss important information. Also, if you are working on a team these tables are absolutely essential for communication and collaboration. To set up your table, first identify the number of columns you think you will need. I usually start with seven. You can add more later if you need to, but I find it easier to remove information before publication than to add it. The headings in your table will depend on the information you need to collect, which depends on the purpose of your review. In this video I will go over the ones I recommend using as well as a few other helpful options. In the first column, always list the author and the year of publication. To make things easier, you will also want to save your articles in a folder on your hard drive by the author and year of publication. I will often also note the country that the study was conducted in. That way it is easy for me to quickly identify if more research is needed in my country specific to the topic of inquiry. You can also note the country later in the table. Discipline may also be useful to note either in the same column or a separate one if you are looking at a multi-disciplinary topic such as hand hygiene practices. It can help you identify if you need to consider looking in other areas to capture missing disciplines or if there is a lack of evidence particular to a discipline. However, if your literature review is focused on a particular discipline, such as nursing, than this information would not add anything to your table and should not be used. Remember to keep your table as concise as possible. Include the topic or focus of the study as well as the research purpose or research question in the next column. The focus of the article is absolutely critical to your summary table. Remember to be concise and specific. I also like to quote the purpose of the article here. Noting the conceptual or theoretical framework will help to inform you of the perspective the researchers are taking. You may also notice common ones that you could consider for your future research proposal. In any review it is important to note the paradigm and methods used. Typically, for first year students I only expect them to identify the paradigm as Qualitative or Quantitative. In upper years of the program and when I publish I expect a more specific identification of the methodology. Sometimes, depending on the purpose of the review, I use separate columns for the design, sampling method and data collection or analysis methods. For pragmatic reasons I still limit the total number of columns in my table to seven or eight. The context, setting and sample should also be noted. This is another location that the country that the study was conducted in can be listed. Just don't put the same information in two spots. Be concise and consistent. Whenever you are putting more than one type of information in a column make sure you are also consistent in the way and order it is listed. For example, always note the setting then the sample in this column. Use a bulleted list or separate information by paragraphs or periods. Key Findings need to be presented in a brief way. Make sure you are not simply writing everything down. What findings are of particular interest to the focus of your literature review? The more concise you are the better. Stay focused. Noting the gaps in the research will help you think about what research needs to be done. Make note of the limitations of the study you are reading as well as areas for future research. This step can be particularly useful when laying the foundation for your next research project. Many published reviews now include all or part of these summary tables. Go take a look at what has been published for more examples of how to construct your table. Music By http://instrumentalsfree.com
Views: 57631 NurseKillam
Exploratory Data Analysis
 
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Dr. Brian Caffo from Johns Hopkins presents a lecture on "Exploratory Data Analysis." Lecture Abstract Exploratory data analysis (EDA) is the backbone of data science and statistical analysis. EDA is the process of summarizing characteristics of a data set using tools such as graphs and statistical models. EDA is a principal method for creating new hypotheses or determining basic empirical support for evolving existing hypotheses. EDA often yields key insights, especially those provided by plots and graphs, where key insights often hit you right between the eyes. In addition, new technology, such as interactive graphics, is greatly enabling EDA. However, care must be taken in EDA to not over-interpret the degree of confirmatory force of conclusions and to avoid attaching strict inferential interpretations to results. This lecture covers the basics of EDA, summarizes some key tools and discusses its role in inference. View slides https://drive.google.com/open?id=0B4IAKVDZz_JUbTVYWVlwZHZkUzA About the Speaker Brian Caffo, PhD received his doctorate in statistics from the University of Florida in 2001 before joining the faculty at the Johns Hopkins Department of Biostatistics, where he became a full professor in 2013. He has pursued research in statistical computing, generalized linear mixed models, neuroimaging, functional magnetic resonance imaging, image processing and the analysis of big data. He created and led a team that won the ADHD-200 prediction competition and placed twelfth in the large Heritage Health prediction competition. He was the recipient the Presidential Early Career Award for Scientist and Engineers, the highest award given by the US government for early career researchers in STEM fields. He co-created and co-directs the SMART (www.smart-stats.org) group focusing on statistical methodology for biological signals. He also co-created and co-directs the Data Science Specialization, a popular MOOC mini degree on data analysis and computing having over three million enrollments. Dr. Caffo is the director of the graduate programs in Biostatistics and is the recipient of the Golden Apple teaching award and AMTRA mentoring awards. Join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
How to calculate Standard Deviation and Variance
 
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Tutorial on calculating the standard deviation and variance for statistics class. The tutorial provides a step by step guide. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos: How to Calculate Mean and Standard Deviation Using Excel http://www.youtube.com/watch?v=efdRmGqCYBk Why are degrees of freedom (n-1) used in Variance and Standard Deviation http://www.youtube.com/watch?v=92s7IVS6A34 Playlist of z scores http://www.youtube.com/course?list=EC6157D8E20C151497 David Longstreet Professor of the Universe Like us on: http://www.facebook.com/PartyMoreStudyLess Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/ MyBookSucks.Com
Views: 1368171 statisticsfun
What is Analysis?
 
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Table of Contents: 00:02 - What is Analysis? 01:30 - Analysis Axe 01:36 - The 4 Ways to Chop Literature and Write About It 02:00 - AXES 03:31 - In 1102
Views: 682 Lisa Russell
weka hadoop big data analysis projects
 
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Contact Best Hadoop Projects Visit us: http://hadoopproject.com/
Views: 110 Hadoop Solutions
What Is An Example Of Summarizing?
 
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Examples of effective summaries and paraphrases (mla style) category integrate evidence summarize & paraphrase sources hits 33427 sep 16, 2009 how to say the same thing in fewer words tutorial leave out any illustrative examplessummarizing example section outline for thurberbotany class (he couldn't see through microscope)economics (the football player math. Summarize example sentences summarizing is defined as taking a lot of information and creating condensed version that covers the main points. Example of summarizing an essaysummarizing a paragraph long beach city college. In addition, summarizing includes condensing the source material into just a few lines. Summarization is also used as a way to close session. The examples used on this page refer to an interpretation of a literary text (sophocles' antigone) but basics summarizing when you summarize source, articulate its basic argument and essential here is example clear succinct summary study vocabulary in context. Not only has the number of graduates in traditional tips on summarizing. Apr 26, 2017 include author tags. For example client ''apr 10, 2017. The word 'summarize' in example sentences page 1. Definition using an author's language word for how to use summarize in a sentence. Use summarize in a sentence summarizing dictionary definition example of paragraphexamples youtube. ('according to ehrenreich' or 'as ehrenreich explains') to remind the reader that you are summarizing the author and the text, not giving your own ideas Avoid summarizing specific examples or data unless they help illustrate the thesis or main idea of the text aug 10, 2016 sample essay for summarizing, paraphrasing, and quoting examples of each task are provided at the end of the essay for further reference both involve taking ideas, words or phrases from a source and crafting them into new sentences within your writing. Summarize tables value by street format nolist total title 'home jul 25, 2006 quoting, paraphrasing, and summarizing. Examples of effective summaries and paraphrases (mla style). Summary using evidence academic guides at walden university. Eng 1001 quoting, paraphrasing, and summarizing ivcc. Creating a grouped summary report. In academic writing, there are a few here's an example of good summary from mizuki's paper original despite decades research examples using direct quotes,word for word quotation (direct quote). Example (summarize command) ibm. An example of summarizing is writing a three paragraphpeanuts and american culturejackson, ms up mississippi, 1990 summarization can help the client to decide which topic most important. Example sentences with the word summarize. How to summarize tutorial and guidance notes mantex. Original example (summarize command). How to summarize a paragraphpreview and read example of summary. Whether paraphrasing or summarizing, credit is always given to the author an example of summarizing original text america has changed dramatically during recent years. Ex
Views: 152 I Question You
Towards a Generic Framework for Table Extraction and Processing - Roya Rastan UNSW
 
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Large volumes of textual data are produced by companies through various media outlets. But the format and quality of data produced varies greatly between the data source outlets, making it difficult for effective and efficient access to the data for meaningful analysis (e.g., how do we answer a question like 'What articles today are about the profitability of Rio Tinto?', 'Is this new good or bad for Rio Tinto?'). However, the wealth of information present in the data can be explored via various text based analysis methods such as keyword search, concept analysis and entity recognition / resolution, sentimental analysis and so on. Therefore, there should be a solution for this problem. A part of this thesis particularly aims to solve table extraction problem associated with PDF file format of Australian company announcements for Sirca. Some of these files often contain market sensitive information presented as tables and the financial users will benefit from quickly gaining access to the data and use it in various search and analysis tasks. On the other hand, PDF files are usually unstructured and this makes texts and data structures (such as tables, graphs, diagrams) recognition and extraction so difficult. Since, successful extraction is the fundamental prerequisite for accurate financial interpretation, this project will provide a solution to identify useful data structures from input files and then automatically import the extracted structures into Table Base (tables repository), to add annotations and manage annotations to enhance the semantic quality of the data collected at different levels of data and structures, and to enable sophisticated reasoning/analysis tasks over the extracted structures. As a result of this process, it also will be shown that having an integrated (semantic) data platform will better enable other textbased analysis methods. So far, table detection phase are implemented completely and table extraction is being implemented. We look at the table extraction problem from the process point of view and propose a table extraction workflow, which can be considered as a plug and‐play architecture for table extraction. The next phase of the project is to find an automatic way for tables' header detection with less user interaction, which enables us to interpret tables and go another step further to more automatic table understanding.
Science Beam – using computer vision to extract PDF data
 
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There’s a vast trove of science out there locked inside the PDF format. From preprints to peer-reviewed literature and historical research, millions of scientific manuscripts can only be found in a print-era format that is effectively inaccessible. A move away from PDF and toward a more open and flexible format like XML would unlock a multitude of use cases for the discovery and reuse of existing research. We are embarking on a project to convert PDF to XML and improve the accuracy of the XML output by building on existing open-source tools. One aim of the project is to combine some of these tools in a modular conversion pipeline that achieves a better overall conversion result compared to using the tools on their own. In addition, we are experimenting with a novel approach to the problem: using computer vision to identify key components of the scientific manuscript in PDF format. We are calling on the community to help us move this project forward. We hope that as a community-driven effort we’ll make more rapid progress towards the vision of transforming PDFs into structured data with high accuracy. You can explore the project on GitHub: https://github.com/elifesciences/sciencebeam. Your ideas, feedback, and contributions are welcome by email to [email protected] Read More about Science Beam Project https://researchstash.com/2017/08/05/science-beam-using-computer-vision-to-extract-pdf-data/
Views: 103 Research Stash
Intelligent Exploratory Text Editing
 
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Writing is an iterative process comprising exploratory, drafting, revising, and editing stages. This technology assists writers by doing most of the hard work during the exploratory stage. Relevant papers in the literature are automatically discovered and presented to the writers in visually-appealing forms to aid the exploration process.
Views: 254 Wilson Wong
Thematic Synthesis
 
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Views: 176 Xniu
Training/test data for developing read/write variational autoencoders
 
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A simple open source data set to help people collaborate on developing recurrent variational autoencoders, with the ability to read/write to external memory. A XML parsing, preprocessing and data checking script was written in R. The timeseries data for each line the writter wrote onto the whiteboard, can then be saved in csv format. These files can then be read into Python/Theano or Lua/Torch to construct training/validation/test sets as the user requires. The R, Python and Lua scripts will be posted on Github. For background on generative models & program learning see, Towards more human-like concept learning in machines: Compositionality, causality, and learning-to-learn (http://cims.nyu.edu/~brenden/LakePhDThesis.pdf) - Brenden Lake - PhD thesis, Massachusetts Institute of Technology, 2014. Concept learning as motor program induction: A large-scale empirical study (http://www.cs.toronto.edu/~rsalakhu/papers/LakeEtAl2012CogSci.pdf) - Brenden M. Lake, Ruslan Salakhutdinov and Joshua B. Tenenbaum DRAW: A Recurrent Neural Network For Image Generation (http://arxiv.org/abs/1502.04623) - Karol Gregor, Ivo Danihelka, Alex Graves, Daan Wierstra - this is the only paper which uses both a recurrent variational autoencoder/decoder with a external read/writable memory. Generating Sequences With Recurrent Neural Networks (http://arxiv.org/abs/1308.0850) - Alex Graves Neural Turing Machines (http://arxiv.org/abs/1410.5401) - Alex Graves, Greg Wayne, Ivo Danihelka Neural Variational Inference and Learning in Belief Network (http://arxiv.org/abs/1402.0030) - Andriy Mnih, Karol Gregor For a desciption and access to the online handwritting dataset go here (http://www.iam.unibe.ch/fki/databases/iam-on-line-handwriting-database) For a description and to download the Hutter prize dataset used in section 3 of [Gra13] go here (http://mattmahoney.net/dc/textdata.html) and for the latest compression benchmarks here (http://mattmahoney.net/dc/text.html).
Views: 913 Ajay Talati
Mining Your Logs - Gaining Insight Through Visualization
 
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Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 25117 GoogleTechTalks
How to analyze the scientific literature yourself - TYT Science
 
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Mike from TYT Science demonstrates an easy-to-use online tool that lets you summarise all the academic literature stored in the US National Library of Medicine. See how to quickly generate graphs about any subject of interest, or profile academic journals and scientists. Have fun! (tool = MEDSUM: www.medsum.info database = PubMed/MEDLINE: www.pubmed.com) Leave comments below Send your clip to TYT Nation at http://upload.theyoungturks.com
Views: 335 TYT Nation
SPSS Questionnaire/Survey Data Entry - Part 1
 
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How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 456280 Quantitative Specialists

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