PPT of twitter sentiment ananlysis

1. Progressive Education Society's
Modern College of Engineering,
Pune-5
Department of MCA
Seminar Presentation
On
Twitter Sentiment Anaysis
By
Vrushank Chaphadkar
52110
Under the Guidance of
Dr.Prakash Kene
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PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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2. INTRODUCTION
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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1.Twitter sentiment analysis involves analyzing and understanding the sentiment expressed in
tweets posted on the social media platform Twitter.
2.It aims to determine whether tweets convey positive, negative, or neutral sentiment towards a
particular topic, product, event, or entity.
3.Sentiment analysis on Twitter is essential for businesses, organizations, and researchers as it
provides valuable insights into public opinion, customer feedback, and brand perception.
4.It employs natural language processing (NLP) and machine learning techniques to automatically
classify and analyze the sentiment of tweets.
5.Twitter sentiment analysis can help in monitoring social trends, measuring the success of
marketing campaigns, identifying customer sentiment towards products or services, and predicting
public opinion during events or crises.
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3. ARCHITECTURE
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PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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4. How the system works?
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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5. STEP 1 :
Live Data Fetching
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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1.Twitter developer account can be used for live
tweet retrieval.
2.This account has consumer key
3.This key is used for accesing live tweets

6. STEP 2 :
PRE-PROCESSING
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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• We converted the uppercase letters of the Tweets to lower case order.
• We removed all user names, URLs and unnecessary white spaces from Tweets.
• Stop words are removed from Tweets.

7. STEP 3 :
Folding Data
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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We used live
dataset(tweets)
for training and
testing through
API calls.

8. STEP 4 :
Training and testing data
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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• Machine learning
sentiment analysis
requires a set of tweets
labelled positive,
negative or objective.
• We have fetched live
tweets and labelled them
based on the number of
positive and negative
words.

9. STEP 5 :
KEYWORD EXTRACTION
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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For each tweet, if a keyword is
present, we mark it as 1, otherwise
marked as 0. Each tweet can be
thought of as a bunch of 0s and 1s,
and based on this pattern, the tweet
is labelled as positive, negative or
neutral. Using TF-IDF (term
frequency- inverse document
frequency) vectorizer, we
vectorized the input tweets.

10. STEP 6 :
Analysis and Classification
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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The training tweets were retrieved
from the live database(twitter API),
classified and run through 2 ML
classifying techniques, to compare
and contrast the performance of
those algorithms. After classifier
has been trained, the test tweets
which were pre-processed and its
keyword extracted, were run
through the classifier to detect the
polarity. After the polarity has been
detected, it was used to display
whether the tweet is positive or
negative.

11. STEP 7 :
EVALUATION
PES MODERN COLLEGE OF ENGINEERING, MCA DEPARTMENT, A.Y. 2022-23
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We calculated the accuracy of the
classifier, mean,sum which would
display polarity and subjectivity of
tweet.