big data: data wrangling boot camp r sentiment...

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Intro. Preview Sent. analysis System req. Hands-on Q & A Conclusion References Files

Big Data: Data Wrangling Boot CampR Sentiment Analysis

Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhD

24 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 201824 February 2018

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Table of contents (1 of 1)

1 Intro.2 Preview

Things that will behappening todayHow we’ll get there

3 Sent. analysisWhat is it, and why shouldI care?A visualization

4 System req.

And implementation

5 Hands-on

6 Q & A

7 Conclusion

8 References

9 Files

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What are we going to cover?

Look to the future

Talk briefly about sentimentanalysis

Address the polyglot of computerlanguages

Talk about our sentiment analysissystem

Data wrangle tweets using R

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Things that will be happening today

Things that we will be doing.

1 Data wrangle tweets usingR

2 Conduct sentiment analysison tweets

3 Look at the sentiments indifferent ways

Sentiments over time.

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Sentiment by sending device

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Sentiment by geographic location

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How we’ll get there

How we’ll get to the images

We’ll walk before we run.

Start with a replay file

Data wrangle using thelibrary file

Go live and download livetweets

Data flows through the backend,into the database, out thefrontend.

The software design document(attached) contains lots of

details.

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Same image.

The software design document (attached) contains lots of details.

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What some of the files do:

Replay file – previously recordedtweets

Configuration file – directivesused by both backend andfrontend script files

Shared library file – routines thatare common to backend andfrontend script files

Backend file – script file thatpopulates the database from thereplay file, or the Internet

Frontend file – script file thatextracts data from the databaseand presents results

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What is it, and why should I care?

A working definition

“Sentiment analysis (also known as opinion mining)refers to the use of natural language processing, textanalysis and computational linguistics to identify andextract subjective information in source materials.Sentiment analysis is widely applied to reviews and socialmedia for a variety of applications, ranging frommarketing to customer service.”

W. Staff [3]

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More formal definitions

“The field of opinion mining and sentiment analysis iswell-suited to various types of intelligence applications.Indeed, business intelligence seems to be one of the mainfactors behind corporate interest in the field.”

Pang and Lee [2]

“Sentiment analysis, also called opinion mining, is thefield of study that analyzes peoples opinions, sentiments,evaluations, appraisals, attitudes, and emotions towardsentities such as products, services, organizations,individuals, issues, events, topics, and their attributes.”

Liu [1]

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Our approach to sentiment analysis

We will:

1 Search the “twitterverse”for tweets using specifichashtags

2 Tokenize each tweet3 Data wrangle each token4 Remove all stop words

from the tokens

5 Count number of positiveand negative tokens

6 Compute the positive,negative, or neutralsentiment for the tokens

7 Display the results

Our approach is language agnostic.

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A visualization

A “Universe” of words

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A visualization

Some words are “Positive”

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A visualization

Some words are “Negative”

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A visualization

A tweet will/may have positive and negative words

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A visualization

Some words we don’t care about

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A visualization

Mechanically this is what we are doing

The steps are:

1 Break the tweet into tokens

2 Remove stop words from the tokens

3 Compute the percentage of remaining tweet tokens that arepositive

4 Compute the percentage of remaining tweet tokens that arenegative

5 Classify the tokens as positive, negative, or neutral

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A visualization

Mathematically this is what we are doing

The steps are:

tokens = {words in tweet}tokensLessStop = tokens − stopWords

positivePart = positiveWords ∩ tokensLessStopnegativePart = negativeWords ∩ tokensLessStop

classification =

positive,ifpositiveThreshold ≤ positiveParttokensLessStopANDnegativePart

tokensLessStop < negativeThreshold

negative,ifnegativeThreshold ≤ negativeParttokensLessStopANDpositivePart

tokensLessStop < positiveThreshold

neutral , otherwise

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And implementation

A software design document

The document contains:

1 Overall system design

2 Algorithms used through outthe system

3 Details about theconfiguration file

4 Details about the databasetables

The file is attached.

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And implementation

What will happen to the tweets when we do nothing.

A histogram of how often atoken occurs.

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And implementation

Same image.

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And implementation

What will happen to the tweets when we makeeverything the same case

Changing case is easy, unlessthey are emojois.

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And implementation

Same image.

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And implementation

What will happen to the tweets we remove non-ASCII

We need to talk about ASCII andthe rest of the character systems.

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And implementation

Same image.

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And implementation

What will happen to the tweets when we remove “stopwords”

Stop words are words/tokensthat have no use in whatever weare doing. Stop words aredomain specific.And we change case, removenon-ASCII, remove punctuation,etc.

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And implementation

Same image.

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Using Rstudio

1 Set the session working directory2 Load the following files into the editor:

1 checkPostgres.R2 backEnd.R3 config.txt4 frontEnd.R5 library.R

3 Run the checkPostgres.R script to ensure PostGres isconfigured correctly

4 Run the backEnd.R script to populate the database with“canned” data

5 Run the frontEnd.R script to analyze the tweets

6 Modify the config.txt file, and re-run back and front ends

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Q & A time.

Q: How did you get into artificialintelligence?A: Seemed logical – I didn’t haveany real intelligence.

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What have we covered?

A preview of today’s activitiesAn overview of the sentimentanalysis

Next: Hands on analysing tweets with R.

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References (1 of 1)

[1] Bing Liu, Sentiment analysis and opinion mining, 2012.

[2] Bo Pang and Lillian Lee, Opinion mining and sentimentanalysis, 2008.

[3] Wikipedia Staff, Sentiment analysis,https://en.wikipedia.org/wiki/Sentiment_analysis,2016.

https://en.wikipedia.org/wiki/Sentiment_analysis

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Files of interest

1 Software designdocument

ODU Big Data, Data Wrangling Boot CampSoftware Overview and Design

Chuck Cartledge

February 19, 2018

Contents

List of Tables ii

List of Figures ii

1 Introduction 1

2 Software system design 32.1 Twitter software front and back ends . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.1.2 Design limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 NASA reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Configuration file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 References 12

A Database tables 13A.1 Twitter related tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13A.2 NASA related tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

B Notational data structures 13B.1 Twitter structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13B.2 Senate bill structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

C Software on each workstation 15

D Files 17

i

List of Tables

1 Frontend and backend algorithm cross matrix. . . . . . . . . . . . . . . . . . 42 Configuration file entries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Tables to support Twitter analysis. . . . . . . . . . . . . . . . . . . . . . . . 134 Tables to support NASA analysis. . . . . . . . . . . . . . . . . . . . . . . . . 135 Notional plotting data structure. . . . . . . . . . . . . . . . . . . . . . . . . 146 Notional plotting data structure. . . . . . . . . . . . . . . . . . . . . . . . . 14

List of Figures

1 Notional data science data flow. . . . . . . . . . . . . . . . . . . . . . . . . . 22 Twitter system design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Image from the “checkPostgres.R” script. . . . . . . . . . . . . . . . . . . . . 18

List of Algorithms

1 Process configuration file. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Update database with new data. . . . . . . . . . . . . . . . . . . . . . . . . . 63 Normalize text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Evaluate text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Update display with new data. . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Plotting hash tag based Tweet sentiment. . . . . . . . . . . . . . . . . . . . . 97 NASA report processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

ii

1 Introduction

The tweet sentiment analysis software used as part of the Old Dominion University College ofContinuing Education and Professional Development Big Data: Data Wrangling boot camp1

will be used to provide boot-camp attendees with hands-on experience doing data-wranglingof textual data.

“We define such data wrangling as a process of iterative data exploration andtransformation that enables analysis. . . . In other words, data wrangling is theprocess of making data useful.”

Kandel et al. [2]

In the boot-camp, we will be:

• Looking at tweets to conduct sentiment analysis relative to arbitrary hashtags,

• Extracting data from static web pages based on cascading style sheets (CSS), and

• Extracting data from a NASA textual archive using the Open Archives Initiative Pro-tocol for Metadata Harvesting (OAI-PMH).

We will be focusing on data wrangling (see Figure 1) using the R programming language.Each boot-camp workstation will have the same software load (see Section C), and almost

fully functional software in R. The twitter software will be complete, in that it will:

• Retrieve tweets from Twitter,

• Place tweets in a PostGres database,

• Retrieve tweets from the database,

• Tokenize the tweets,

• Qualify the tweets as positive, negative, or neutral, and

• Plot the results in different ways.

The CSS software will be complete, in that it will:

• Download a “hard coded” web page,

• Extract a data field based on a CSS selector,

• “Wrangle” the data as necessary,1https://www.odu.edu/cepd/bootcamps/data-wrangling

https://www.odu.edu/cepd/bootcamps/data-wrangling

Figure 1: Notional data science data flow. Data wrangling requires domain specific knowl-edge to cleanse, merge, adapt, and evaluate raw data. Image from [2].

2

• Present the data.

The chrome browser and the SelectorGadget plugin2 will be used to identify CSS selectors.The OAI-PMH software will be complete, in that it will:

• Download technical report meta data from the NASA Technical Reports Server3,

• Insert document IDs, report titles, and report descriptions into a PostGres database,

• Generate a static web page based on searching the PostGres data.

The code will be modified to display information about the reports based on how the textualdata is wrangled.

Data wrangling will focus on:

1. Identifying problems with the tweet tokens,

2. Developing solutions to those problems, and

3. Reducing the number of problematic tokens.

The remaining sections layout in detail the overall system design, details of the majoralgorithms, database tables, and the configuration file used to control the system.

2 Software system design

2.1 Twitter software front and back ends

2.1.1 Details

The sytem is logically divided into three parts (see Figure 2):

1. A “backend” that gets tweets from Twitter or a data file.

2. A database to hold tweets from the backend.

3. A “frontend” that retrieves data from the data base for analysis and display.

4. The frontend and backend processes are controlled by the contents of a configurationfile (see Section 2.3).

Details of the various algorithms used by the backend and frontend processes are outlinedin this section.

2https://chrome.google.com/webstore/detail/selectorgadget/mhjhnkcfbdhnjickkkdbjoemdmbfginb?

hl=en3https://ntrs.nasa.gov

3

https://chrome.google.com/webstore/detail/selectorgadget/mhjhnkcfbdhnjickkkdbjoemdmbfginb?hl=en


https://ntrs.nasa.gov

Figure 2: Twitter system design. Both front and back ends read data from a commonconfiguration file, and use a shared library file of common functions.. The back end willreceive data from the internet or from a replay file, based on directives in the configurationfile and update the database with new data. The front end will connect to the database andretrieve data based on directives from the configuration file.

Table 1: Frontend and backend algorithm cross matrix. Alogrithms that are used by boththe front and back ends are recommended for a “utillity” file or library that can be accessedby both ends.

Num. Name Back end Front end

1 Process configuration file. X X

2 Update database with new data X

3 Normalize text X

4 Evaluate text X

5 Update display with new data X

6 Plotting hash tag based Tweet sentiment X

4

Input: Location of configuration fileAssign default values;while not at the end of the file do

read line;if not a comment line then

get token;get value;if is a Hashtag then

add value to list of hashtags;else

structure token value = value;end

end

endResult: A language specific data structure, values from file override defaults.

Algorithm 1: Process configuration file.

2.1.2 Design limitations

The current design polls Twitter for new tweets on a periodic basis. The entire list of searchhash tags are polled, any returned tweets are stored in the database, and the system “sleeps”for a number of seconds (as per the configuration file). There are a number of factors thataffect this processing cycle, including:

1. The number of hash tags being queried. Each poll takes a finite amount of time, evenif no tweets are returned, so the more hash tags being queried, the longer it will taketo service the complete list of tags.

2. Each tweet becomes a single row in the database. The more tweets that are returnedfrom the query, the longer it takes to update the database with all the tweets.

3. Each tweet has a unique serial number. Each query includes the serial number of theearliest (the one furthest in the past) one of interest in order to get a complete andcontinuous tweet stream for the hash tag. The earliest acceptable tweet is updatedafter a successful query.

4. The no-cost query capability is limited to 100 tweets per query. If more than 100 tweetsare created between queries, then the polling process will continue to fall further andfurther behind.

Because of these design and implementation limitations, if tweets are being created fasterthan the polling process can collect them, then the system will fall further and further behind.

5

Input: Language specific configuration structurestart = first time in data file;if Offset = TRUE then

diff = now() - start;else

diff = 0endtime end = start + SleepyTime + diff;for Polls remaining do

if Live thensubmit query to Twitter;request data from Twitter;for lines from Twitter do

extract time from JSON;data = base64 encoding of entire JSON;insert time and data into database;if CollectionFile is not NULL then

append time and data to CollectionFile;end

endsleep SleepyTime;

elseread line from file;parse line into time and data;while time > time end do

time end = time end + SleepyTime;sleep SleepyTime;

endinsert time and data into database;

end

endResult: An updated database.

Algorithm 2: Update database with new data.

6

Input: Text to be “normalized”, “stop word list”cleansed = Null;for Text do

lower case Text;remove non-ASCII;stemming;if Text not in “stop word list” then

append Text to cleansedend

endreturn cleansed ;Result: Normalized text

Algorithm 3: Normalize text.

Input: sourceText, baseLineTextnumberOfSourceWords = number of words in baseLineTex;percentage = numberOfSourceWords / numberOfWordsInSourceText;return percentage;Result: Percentage of source text in baseLineText

Algorithm 4: Evaluate text.

The limitations imposed by a polling interface can be overcome by using a streaminginterface4. A polling interface was used because it is simple to design, simple to implement,and simple to test. The back-end process could be replaced by a streaming interface withoutaffecting the front-end process.

2.2 NASA reports

Processing the NASA reports is straight forward (see Algorithm 7).

4 R example:http://bogdanrau.com/blog/collecting-tweets-using-r-and-the-twitter-streaming-api/

7

http://bogdanrau.com/blog/collecting-tweets-using-r-and-the-twitter-streaming-api/

Input: Language specific configuration structurecleansedPositive = normalize positive words;cleansedNegative = normalize negative words;cleansedStopWords = normalize Stop words;timeStart = minimum time from database ;for Polls remaining do

timeEnd = timeStart + SleepyTime;hash tag new data = NULL;lines = query database from timeStart to timeEnd;for lines do

tweet = base64 decode of data;if parse Tweet is GOOD then

extract text;extract hash tag from Tweet text;cleansedText = normalized text less cleansedStopWords;positive percentage = evaluate cleansedText vs. cleansedPositive;negative percentage = evaluate cleansedText vs. cleansedNegative;neutral percentage = 100 - positive percentage - negative percentage;update plotting information (hash tag, source, location);

end

endplot hash tag results;timeStart = timeEnd;sleep SleepyTime;

endplot source information;plot location information;Result: An updated display.

Algorithm 5: Update display with new data.

8

Input: The previous/current plotting data, and new datafor Each Tweet type do

set the lower left polygon point as the previous poll and the last previous typecount;

set the upper left polygon point as the previous poll and the last previous typecount + next previous type count;

set the lower right polygon point as the current poll and the current type type;set the upper right polygon point as the current poll and the current type count +next current type count;

plot the polygon, filling it with then Tweet type colorendfor Each Tweet type do

set previous Tweet count value to current Tweet count value;endResult: An updated display data structure, and display.

Algorithm 6: Plotting hash tag based Tweet sentiment. From the user’s perspective, astacked histogram is plotted. From a programatic perspective, each three filled polygonsare plotted where the left and right edges are the poll number, and the vertical componentis the number of Tweets per type (positive, neutral, and negative). The display will showthe absolute number of Tweets, and the color bands will show the proportions of each type.

Input: The contents of the configuration file.if Reset the database then

create necessary database tables ;populate the database with report data ;

endupdate the database tokens based on database data ;define a search term ;normalize the search term ;search the database for documents that match the normalized term ;create an html file based on the results ;Result: An updated html file showing the query results.

Algorithm 7: NASA report processing.

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2.3 Configuration file

Software processes are coordinated by control values in a shared configuration file.

1. A common configuration file to be used by both the data capture and the data presen-tation programs.

2. The file will default to a “well known” name in a “well known” location.

3. An alternative file can be passed in as a command line argument.

4. Any line in the file starting with a hashtag (#) will be treated as a comment and notprocessed.

5. File entries are case sensitive.

6. All entries are optional. Some are required for live operation capture.

7. If the same option appears more than once, the last option will be honored, exceptfor hashtags. Hashtags will be treated as a collective.

8. “White space” separates each token from its value.

Table 2: Configuration file entries. The default stop word file will be provided (source: http://xpo6.com/list-of-english-stop-words/). It can be modified or replaced as needed.

Token Meaning Default

APIPrivateKey Twitter private API key. Must be supplied forlive operation.

(None)

APIPublicKey Twitter public API key. Must be supplied forlive operations.

(None)

CollectionFile A file to collect raw Tweets during live opera-tions.

(None)

ColorNegative The color used to indicate negative tweets. BLACK

ColorNeutral The color used to indicate neutral tweets. WHITE

ColorPositive The color used to indicate positive tweets. GREEN

Hashtag This is the hashtag used to search Twitterwithout the leading hashtag (#).

(None)

LexiconFile A text file containing positive and negativewords.

lexicon.csv

(Continued on the next page.)

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http://xpo6.com/list-of-english-stop-words/

http://xpo6.com/list-of-english-stop-words/

Table 2. (Continued from the previous page.)


Offset Should the replay data be brought forward tocurrent time? Accepted values are TRUE orFALSE.

FALSE

Poll How many times to add new data to thedatabase. If data is being replayed, the maxi-mum number of database updates will be thisvalue, or the end of data from the file. If liveoperations, then this is how many times Twit-ter will be polled for new data.

10

PostgresTable The Postgres table containing the tweets. tweeets

PostgresUser The Postgres user name used to access thedatabase.

openpg

PostgresPassword The Postgres password associated with thePostgres user.

new user password

PostgresTableNASA The Postgres table containing NASA technicalreport related data.

NASAReports

ResetDatabase Should the database be reset, and all previousdata lost when the program starts. Acceptedvalues are TRUE or FALSE.

FALSE

ResetDatabaseNASA Should the NASA technical report databasebe reset, and all previous data lost when theprogram starts. Accepted values are TRUE orFALSE.

FALSE

SleepyTime How many seconds between updates to thedatabase. It is possible that no data will beadded to the database if there isn’t any Twit-ter activity for a hashtag.

5

SourceFile The file containing the data to be replayed.If this option is not set, then the operation isassumed to be “live.”

(None)

StopwordsFile The file containing “stop words” that will notbe considered in determining positive or neg-ative sentiment.

stopword.txt

(Continued on the next page.)

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Table 2. (Continued from the previous page.)


ThresholdNegative The percentage of words in a tweet considerednegative for the tweet to be labeled negative.

0.33

ThresholdPositive The percentage of words in a tweet consideredpositive for the tweet to be labeled positive.

0.33

(Last page.)

3 References

[1] Simon Josefsson, RFC 4648: The Base16, Base32, and Base64 Data Encodings, RFC4648, RFC Editor, October 2006.

[2] Sean Kandel, Jeffrey Heer, Catherine Plaisant, Jessie Kennedy, Frank van Ham,Nathalie Henry Riche, Chris Weaver, Bongshin Lee, Dominique Brodbeck, and PaoloBuono, Research directions in data wrangling: Visualizations and transformations forusable and credible data, Information Visualization 10 (2011), no. 4, 271–288.

12

A Database tables

A.1 Twitter related tables

These are the database tables/data structures to support Twitter analysis:

Table 3: Tables to support Twitter analysis.

Column Meaningtime Unix seconds as extracted from the Tweet.data Base 64 encoding of the entire JSON Tweet.

A.2 NASA related tables

These are the database tables/data structures to support NASA analysis:

Table 4: Tables to support NASA analysis.

Column Meaningid NASA document ID from NTRS.title Base 64 encoded report title.description Base 64 encoded report description.tokens “Normalized” tokens based on raw title and description.

“Base encoding of data is used in many situations to store or transfer datain environments that, perhaps for legacy reasons, are restricted to US-ASCIIdata. Base encoding can also be used in new applications that do not have legacyrestrictions, . . . ”

S. Josefsson [1]

Base 64 encoding ensures that data can pass cleanly through PostGres operations.

B Notational data structures

B.1 Twitter structures

These are the notational data structures used by the various processes.

13

Table 5: Notional plotting data structure. A multidimensional structure indexed by hashtag.

Name PurposePositiveTweetSource A dictionary/hash table to keep track of the

number of positive Tweets by software source.This is for the entire polling period.

NegativeTweetSource A dictionary/hash table to keep track of thenumber of negative Tweets by software source.This is for the entire polling period.

PositiveTweetLocation A dictionary/hash table to keep track of thegeographic location of a positive Tweet.

NegativeTweetLocation A dictionary/hash table to keep track of thegeographic location of a negative Tweet.

Table 6: Notional plotting data structure. This structure is indexed by hashtag.

Cell Use0 Number of positive Tweets.1 Number of neutral Tweets.2 Number of negative Tweets.

B.2 Senate bill structures

These are the notional data structures associated with the Senate Bills application:

1. Each bill is stored in a separate file on the disk. These files may, or may not bedeleted when the R session ends. Hence, care must be taken with how the R script isexecuted. If the script is executed within an IDE, files may persist for the duration ofthat session. If the script is run using the CLI Rscript mechanism, then the files willbe deleted when the Rscript session ends.

2. Internally, all information of interest is maintained in the list “sponsors” which isorganized like this:

sponsors[[Billnumber]][1 = bill sponsor] [2 ...n cosponsors]

Party affiliation is included in the sponsor/cosponsor string.

14

C Software on each workstation

This section contains the assumptions about the operating system environment, and softwareload out for each work station.

1. Operating system: Windows 7

2. Database

(a) Name: PostgresSQL

(b) Version: 9.5.3

(c) Source: http://www.postgresql.org/download/windows/ and http://www.enterprisedb.com/products-services-training/pgdownload#windows

(d) Superuser password: ODUBootcamp

(e) Misc: It may be necessary to manually start the PostGres server using thesecommands in a terminal window:

cd "\Program Files\PostgreSQL\9.5\bin"

.\pg_ctl -D "c:\Program Files\PostgreSQL\9.5\data" start

3. Software

(a) Chrome browser

• Version: 63.0.3239.132• Available from: https://www.google.com/chrome/browser/desktop/index.html

(b) Java

• Version: Java SE Development Kit 7u79 (assuming Windows 64 bit OS)• Available from: http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html

(c) pgAdmin

• Version: 1.22.1• Available from: https://www.pgadmin.org/download/

(d) R

• Version: 3.3.1• Available from: https://cran.r-project.org/bin/windows/base/• Packages:

15

http://www.postgresql.org/download/windows/

http://www.enterprisedb.com/products-services-training/pgdownload#windows

http://www.enterprisedb.com/products-services-training/pgdownload#windows

https://www.google.com/chrome/browser/desktop/index.html

https://www.google.com/chrome/browser/desktop/index.html

http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html

http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html

https://www.pgadmin.org/download/

https://cran.r-project.org/bin/windows/base/

– bitops

– DBI

– devtools

– ggmap

– ggplot2

– htmltools

– httr

– jsonlite

– mapdata

– mapplots

– mapproj

– maps

– methods

– NLP

– OAIHarvester

– openssl

– RCurl

– rdom

– rjson

– ROAuth

– RPostgreSQL

– rvest

– SnowballC

– streamR

– tm

– tools

– utils

– XML

– xml2

(e) R-Studio

• Version: 0.99.903• Available from: https://www.rstudio.com/products/rstudio/download/

(f) SelectorGadget

• Version: 1.1• Available from Chrome web store: https://chrome.google.com/webstore/detail/selectorgadget/mhjhnkcfbdhnjickkkdbjoemdmbfginb?hl=en

(g) wget

• Version: 1.*• Available from: https://eternallybored.org/misc/wget/

The PATH environment variable should be updated to include the location of the Rinterpreter.

16

https://www.rstudio.com/products/rstudio/download/



https://eternallybored.org/misc/wget/

D Files

A collection of miscellaneous files mentioned in the report.

• installLibraries.R – an R script to install all necessary libraries/packages from “the

cloud”

• checkPostgres.R – an R script to test the PostGres installation (see Figure 3).

A complete collection of files (presentations, data, scripts, etc.) can be downloaded fromthe boot camp web site using this command:

wget -np -r http://www.cs.odu.edu/~ccartled/Teaching/2018-Spring/DataWrangling/

The Windows version of wget sometimes leaves “trashy” files behind, like “index.html@C=D;O=A”and so on. These files are not part of the boot camp web page, and can be removed or ig-nored. None of the boot camp scripts use, or process these files. The *nix version of wgetdoes not leave trashy files.

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## https://gist.github.com/stevenworthington/3178163

rm(list=ls())

latexTable

Figure 3: Image from the “checkPostgres.R” script. This image will be created (sans someof the image decorations) after successful execution of the “checkPostgres.R” script. Thedecorations will change based on how the script was executed.

18

"Chuck Cartledge"

Intro.PreviewThings that will be happening todayHow we'll get there

Sent. analysisWhat is it, and why should I care?A visualization

System req.And implementation

Hands-onQ & AConclusionReferencesFiles

big data: data wrangling boot camp r sentiment...

Documents