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.
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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13. DATA CLEANING
Pre-processing
Tweet Retrieval
Stop
Words
Removal
Filtering and
Tokenizing
Stop words
and Symbols
Emotional
Rules
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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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