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ITNPBD7 Sentiment Analysis
创建日期：2024-07-01 16:15:44
标签： ITNPBD7
Sentiment Analysis
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ITNPBD7 Assignment 
Sentiment Analysis
Your task in completing this assignment is to analyse a range of reviews for the most common words
that appear for both positive and negative sentiments. The data are contained in a file called
sentiments.txt, which you can download from the module assignment page on Canvas (a shorter
version called shortsent.txt is also available for testing purposes). The file contains the type of item
being reviewed (Restaurant, Movie, Product) followed by the review text and then a sentiment value
(1 for positive, 0 for negative). Each review is on a single line of the file with the different fields
separated by a tab character, as shown in the following example:
Restaurant I swung in to give them a try but was disappointed. 0
Restaurant I had a pretty satisfying experience. 1
Movie Some applause should be given to the “prelude”. 1
Product A must study for anyone interested poor design. 0
Your task is to write a Java Hadoop Map/Reduce solution that will, in a single pass, find the 5 most
common words associated with a given item type and sentiment. The result will be 6 rows of data
consisting of the top 5 words for each item type and sentiment score in a form similar to the
following (the format and order can be different but the words for each item/sentiment must be
correct):
Restaurant 0 brother again law night eating
Restaurant 1 you’d any bean fry stir
Product 0 anyone must industrial study interested
Product 1 phone use restored simple performance
Movie 0 enter script watch unethical rated
Movie 1 however both superb rickman complex
In a similar manner to the practicals, you will be required to exclude common words in the reviews
and the words that you should exclude are provided as a link on Canvas in the file exclude.txt. You
could start by hard coding some of these words into your Mapper code but you are eventually
expected to load them from a cache file when the program runs.
Along with your code, you should also submit a short written report, detailing your design and the
results you found.
Step 1, HDFS – 20 Marks
Before you write any code, you will need to copy the data onto your own space in HDFS. In your
report, give details of how HDFS stores data such as this (assume the file is much bigger than it really
is for the purpose of your description). This section should be around half a page long, plus a
diagram. Describe what HDFS is for, the architecture it uses, and the roles of different nodes in the
cluster.
Document all the hdfs commands you used to create a directory for the data and place it your data
on HDFS ensuring you explain why the data must be uploaded in this way. Make sure everything you
put here, including the diagram, is your own work. Do not copy anything from other sources.
Step 2, Design – 20 Marks
Now consider the Map/Reduce design you will implement. You know there are only six different
results that must be produced and a larger (but unknown) number of different words used in those
reviews. In your report, consider and compare two different choices you could make to implement
the given task. What keys and values will the mapper emit? Consider how much data will be moved
across the network in each of your two designs. Also consider how many different reducers will be
used in each case. Finally, choose the more efficient design, implement it and justify your choice.
Step 3, Implement – 60 Marks
Using the SentimentWords.java file provided on the assignment page in Canvas as a starting point,
modify this code to produce the results requested above. This code is just a revised version of the
original WordCount.java file from your practicals and will need significant changes to meet the
desired requirements. It is supplied with the file TestSentiment.java that you can use with the
Hadoop simulator mochadoop to check your logic on the smaller set of data found in shortsent.txt.
Your final output should however be produced from the full sentimentss.txt file that should be run
on the Hadoop server.
案例分类
标签云
关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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