The document discusses enhancing sentiment analysis on tweets. It presents an architecture that extracts raw tweet data, performs data filtering, tokenization, and sentiment classification. Tweets are classified as positive, negative, or neutral. A rule-based approach and emotional rules are used to check polarity. Charts are used to represent the classified sentiment. The objective is to analyze tweets and represent them as charts for particular products.

1. Enhancement of sentiment
analysis on twitter
By:
M.Iswarya

2. CONTENTS
 Sentiment Analysis
 Objective
 Architecture
 Classification of Framework
 Virtualization
 Login Screen
 Data Extraction
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3.  Data Cleaning
 Checking Polarity
 Chart Representation
 Application
 Conclusion
 Future Enchancement
 References
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4. SENTIMENT ANALYSIS
 It is the classification of the polarity of a
given text in the document , sentence or
phrase.
 The goal is determined whether the
expressed opinion in the text is positive
negative and neutral.
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5. OBJECTIVE
• To study the sentiment analysis in
microblogs which in view to analyse the
tweets from the users.
• These tweets are sentimentally analysed
and represented as a chart for a particular
product.
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6. ARCHITECTURE
a
Raw Tweet Data
Injection
Data
Sentiment Classification
Filtering
Tokenization
Stop Words
Symbol
SymbolsSeptember 7, 2017 6Trp Engineering College

7. Symbols
Texual Non
Texual
Twitter Emulator
Texual
Dictionary
Compare
Structured Data
Refine
Output_Tweet
Visualization
Chart
Converter
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8. CLASSIFICATION OF FRAMEWORK
 Proposed System:
Input
(Keywords)
Tweets
Retrieval
Data
Pre-Processing
Classification
Algorithm
Classified
Tweets
Analysis in
Graph
Representation
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9. VIRTUALIZATION
Hortonworks Sandbox
Vm Player
Vmware
virtualization
HDP Access
Login
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10. LOGIN SCREEN
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12. DATA EXTRACTION
a
Public
Tweets
Get the Raw
Tweets
Tweets
Retrieval
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13. DATA CLEANING
 Pre-processing
Tweet Retrieval
Stop
Words
Removal
Filtering and
Tokenizing
Stop words
and Symbols
Emotional
Rules
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16. CHECKING POLARITY
 Rule Based
Emotional
Rules
Positive
Negative
Neutral
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23. CHART REPRESENTATION
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24. PROPOSED METHOD
• Implement HybridSeg approach to finds the optimal
segmentation of tweets
• HybridSeg is generated via named entities extracted from
user’s followees’ and user’s own posts.
• Implement K-nearest neighbor classifier (K-NN) approach to
mining the opinion words
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25. EXISTING SYSTEM
• The problem statement is user’s effort need
more to access to the tweet carrying the
information of interest.
• Difficult to recognize the named entities at the
time of tweet segmentation
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26. APPLICATION
 To Review – Related Websites
Movie Review
Product Review
 Sub-Component
Technology – Context on sensitive Information.
 Business – Knowing Consumers attitude and
trends.
 Public – Opinion on the political leaders
Current issues
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27. HARDWARE REQUIRED
• Processor : Dual core processor 2.6.0
GHZ
• RAM : 1GB
• Hard disk : 160 GB
• Compact Disk : 650 Mb
• Keyboard : Standard keyboard
• Monitor : 15 inch color monitor
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28. SOFTWARE REQUIREMENTS
• Operating system : Windows OS ( XP, 2007,
2008)
• Front End : JAVA
• IDE for JAVA : Eclipse
• Tool : Hadoop
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29. CONCLUSION
 The results of our experiments show that
classifying tweets as “positive”, “negative”
and “neutral”.
 This can use solely the proposed features if
computing resources are concerned such as
micro blogs.
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30. FUTURE ENCHANCEMENT
• In future work, we can extend our
approach implement various classification
algorithm to predict the attackers and also
eliminate the attackers from twitter
datasets.
• And try this approach to implement in
various languages in twitter.
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31. REFERENCES
 Shengnua Liu, Xueqi Cheng, Fuxin Li and Fangtao Li
“TASC:topic-adaptive sentiment classification on dynamic
tweets”IEEE transactions on Knowledge and Data
Engineering,2013.
 Aliza Sarlan, Chayanit Nadam, Shuib Basri.“Twitter sentiment
analysis” International Conference On Information
Technology and Multimedia ,2014.
 Manju Venugopalan, Deepa Gupta “Exploring Sentiment
Analysis” IEEE transactions on Knowledge and Data
Engineering.
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