big data: data wrangling boot camp python sentiment analysisccartled/teaching/2017... · big data:...
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Big Data: Data Wrangling Boot CampPython 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
28 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 201728 January 2017
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Table of contents I
1 Introduction
2 Preview
3 Sent. analysis
4 Languages
5 System req.
6 Sublime IDE
7 Q & A
8 Conclusion
9 References
10 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 Python
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Things that will be happening today
Things that we will be doing.
1 Data wrangle tweets usingpython
2 Conduct sentiment analysison tweets
3 Look at the sentiments indifferent ways
Sentiments over time.
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Things that will be happening today
Sentiment by sending device
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Things that will be happening today
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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How we’ll get there
Same image.
The software design document (attached) contains lots of details.
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How we’ll get there
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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What is it, and why should I care?
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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What is it, and why should I care?
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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Comparing a known language with Python and R
A specific language was not arequirement for this boot camp.Python and R used in boot-camp.Too many languages to compareto Python and R.
http://hyperpolyglot.org/
Use Hyperployglot.org as a cross reference.
http://hyperpolyglot.org/
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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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Basic operations
A complete IDE
A complete Python integrateddeveloment environment (IDE)
1 Text editor
2 Python console
3 Extensible
4 Tabbed display for multiplefiles
We incorporate the Anacondaplugin for additional functionality.
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Basic operations
Editor
“Smart” editor
CTRl + O to open a file
CTRl + S to save a file
CTRl + B to run a file
Multiple files can be openedat once
The Anaconda plugin enhances the editor, and adds “lint” andstyle functions.
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Q & A time.
Q: Do you know what the deathrate around here is?A: One per person.
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What have we covered?
A preview of today’s activitiesAn overview of the sublime IDE
Next: Hands on analysing tweets with Python.
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References I
[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
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ODU Big Data, Data Wrangling Boot CampSoftware Overview and Design
Chuck Cartledge
January 17, 2017
Contents
List of Tables i
List of Figures ii
1 Introduction 1
2 Software system design 32.1 Algorithms used by the software front and back ends . . . . . . . . . . . . . 32.2 Configuration file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3 Design limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 References 11
A Database tables 12
B Notational data structures 12
C Software on each workstation 13
List of Tables
1 Frontend and backend algorithm cross matrix. . . . . . . . . . . . . . . . . . 42 Configuration file entries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Tables to support Twitter analysis. . . . . . . . . . . . . . . . . . . . . . . . 124 Notional plotting data structure. . . . . . . . . . . . . . . . . . . . . . . . . 125 Notional plotting data structure. . . . . . . . . . . . . . . . . . . . . . . . . 12
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List of Figures
1 Notional data science data flow. . . . . . . . . . . . . . . . . . . . . . . . . . 22 System design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
List of Algorithms
1 Process configuration file. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Update database with new data. . . . . . . . . . . . . . . . . . . . . . . . . . 53 Normalize text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Evaluate text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Update display with new data. . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Plotting hash tag based Tweet sentiment. . . . . . . . . . . . . . . . . . . . . 8
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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. [1]
In the boot-camp, we will be looking at tweets to conduct sentiment analysis relative toarbitrary hashtags. We will be focusing on data wrangling (see Figure 1) using both Pythonand R programming languages.
Each boot-camp workstation will have the same software load (see Section C), and almostfully functional software in Python and R. The 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.
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.
1https://www.odu.edu/cepd/bootcamps/data-wrangling
https://www.odu.edu/cepd/bootcamps/data-wrangling
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Figure 1: Notional data science data flow. Data wrangling requires domain specific knowl-edge to cleanse, merge, adapt, and evaluate raw data. Image from [1].
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Figure 2: System design. Both front and back ends read data from a common configurationfile, and use a shared library file of common functions.. The back end will receive data fromthe internet or from a replay file, based on directives in the configuration file and updatethe database with new data. The front end will connect to the database and retrieve databased on directives from the configuration file.
2 Software system design
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 2.2).
The backend receives data from Twitter or a data file, puts the data into the database,and the frontend retrieves the data for processing.
2.1 Algorithms used by the software front and back ends
Details of the various algorithms used by the backend and frontend processes are outlinedin this section.
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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
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.
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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.
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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.
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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.
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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.
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2.2 Configuration file
The software frontend and backend processes are coordinated by control values in a sharedconfiguration 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)
(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/
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Table 2. (Continued from the previous page.)
Token Meaning Default
LexiconFile A text file containing positive and negativewords.
lexicon.csv
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.
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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
ResetDatabase Should the database be reset, and all previousdata lost when the program starts. Acceptedvalues are TRUE or FALSE.
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.
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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
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.)
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2.3 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.
The limitations imposed by a polling interface can be overcome by using a streaminginterface2. 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.
3 References
[1] 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.
2Python example:http://bogdanrau.com/blog/collecting-tweets-using-r-and-the-twitter-streaming-api/,R example:http://bogdanrau.com/blog/collecting-tweets-using-r-and-the-twitter-streaming-api/
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http://bogdanrau.com/blog/collecting-tweets-using-r-and-the-twitter-streaming-api/
http://bogdanrau.com/blog/collecting-tweets-using-r-and-the-twitter-streaming-api/
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A Database 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.
B Notational data structures
These are the notational data structures used by the various processes.
Table 4: 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 5: 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.
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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) Python
i. Interpreter version: 2.7.12
ii. Available from: https://www.python.org/downloads/release/python-2712/
iii. Modules:
• base64• csv• descartes• geos• json• lxml==3.6.0• math• matplotlib• nltk• numpy• os• pickle
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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.python.org/downloads/release/python-2712/
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• psycopg2• shapely3
• sys• time• traceback• tweepy• urllib
(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:
i. bitops
ii. DBI
iii. ggmap
iv. ggplot2
v. httr
vi. jsonlite
vii. mapdata
viii. mapplots
ix. mapproj
x. maps
xi. methods
xii. NLP
3The shapely module depends on a number of other modules and can be difficult to install. The best waythat I’ve found to install the module and all its dependencies is to use the Python and hardware architecturespecific whl file from http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely After downloading the whlfile, it can be installed with using pip, for example:pip install Shapely-1.5.17-cp27-cp27m-win amd64.whl
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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/
http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely
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xiii. openssl
xiv. RCurl
xv. rjson
xvi. ROAuth
xvii. RPostgreSQL
xviii. SnowballC
xix. streamR
xx. tm
(e) R-Studio
• Vesion: 0.99.903• Available from: https://www.rstudio.com/products/rstudio/download/
(f) Sublime
• Version: build 3114• Available from: https://www.sublimetext.com/3
Requires:
• Anaconda plugin– Version: v1.4.4
– Available from: http://damnwidget.github.io/anaconda/
Anaconda can also be installed using Package Control for Sublime Text 3.
i. Install Package Control using the Python script found at:https://packagecontrol.io/installation
in the Sublime Console window.
ii. Using the Package Control capability just added to Sublime, install Ana-conda using the directions found at:http://damnwidget.github.io/anaconda/
basically:Tools → Command Palette → Install Package → anaconda
The PATH environment variable should be updated to include the location of the Rand Python interpreters.
15
https://www.rstudio.com/products/rstudio/download/
https://www.sublimetext.com/3
http://damnwidget.github.io/anaconda/
https://packagecontrol.io/installation
http://damnwidget.github.io/anaconda/
"Chuck Cartledge"
IntroductionPreviewThings that will be happening todayHow we'll get there
Sent. analysisWhat is it, and why should I care?A visualization
LanguagesSystem req.And implementation
Sublime IDEBasic operations
Q & AConclusionReferencesFiles