We're Open
+44 7340 9595 39
+44 20 3239 6980

[Solved]Computer Science. Data Mining from Twitter Using Python

  100% Pass and No Plagiarism Guaranteed

[Solved]Computer Science. Data Mining from Twitter Using Python

The Project Involves Mining data from twitter about apple’s new mobile device the Iphone x. I want to get information on how people feel about the latest apple device flagship and determine whether more people feel good about the device or more people feel bad about the device.

 

 

  • In THIS REGARD, I am to develop a system(program) that would perform sentiment analysis on data(tweets) received from twitter using the Twitter Streaming API. I have already built code for collecting the tweets as I used keywords : ‘iphone x’, ‘iphone 10’, #iphonex, “iphone 10’, ‘iphone ten’ to filter the tweets about the iphonex device. I have also saved the received tweets in a text file called phonecorpus.txt
  • From my understanding so far, the data received from twitter should be stored in a text file for further pre-processing. In order to do sentiment analysis, I would need to pre-process the received tweets. Pre-processing operations include Removal of URLS, Spelling corrections, stopword removal, removal of non-english tweets, removal of punctuations and numbers etc.
  • The data obtained from the streaming API contains many attributes eg( created_at: , location: , favourite_count etc. for the sentiment analysis, I feel only the “text:” field will be needed.
  • The sentiment analysis code should be able to analyze the text file containing the tweets and categorize the tweets into negative and positive tweets. Afterwards, a pie chart should be generated showing the percentage of negative tweets to positive tweets (all by using relevant python libraries). On the pie chart, the positive tweets should be denoted in blue colour and the negative tweets should be denoted in red colour. Also, code should be provided that will count the total number of positive and negative tweets.
  • Lastly, There should be code that would extract URL links for reviews about the iphonex (Please note that the links should be extracted from the text-file containing the twitter data that has been streamed).

 

 

 

NB: Please code should be well commented so I can understand what each block is doing. Also maybe a report saying the steps/techniques carried out would be helpful. For this project I suppose you would use functions to build code. You can explain what each function does. s


100% Plagiarism Free & Custom Written,
Tailored to your instructions


International House, 12 Constance Street, London, United Kingdom,
E16 2DQ

UK Registered Company # 11483120


100% Pass Guarantee

STILL NOT CONVINCED?

View our samples written by our professional writers to let you comprehend how your work is going to look like. We have categorised this into 3 categories with a few different subject domains

View Our Samples

We offer a £ 2999

If your assignment is plagiarised, we will give you £ 2999 in compensation

Recent Updates

Details

  • Title: [Solved]Computer Science. Data Mining from Twitter Using Python
  • Price: £ 149
  • Post Date: 2021-10-06T12:32:35+00:00
  • Category: Assignment Requirements
  • No Plagiarism Guarantee
  • 100% Custom Written

Customer Reviews

[Solved]Computer Science. Data Mining from Twitter Using Python [Solved]Computer Science. Data Mining from Twitter Using Python
Reviews: 5

A masterpiece of assignment by , written on 2020-03-12

I was worried about the plagiarism ration in my dissertation. But thanks to my dedicated writer, I received 0% plagiarism in all the chapters. I owe my writer a million thanks..!
Reviews: 5

A masterpiece of assignment by , written on 2020-03-12

I have been taking help from Insta Research since 2015 and believe me, this place is incredible in giving the best help in assignments and essays. I also ask them to run plagiarism in my essays that I have written, and they always gave me accurate results. I am literally blessed to have a strong bonding with this site so that in any need of urgency, I contact them and find them always beside me. Thank you!