The document discusses sentiment analysis and opinion mining. It describes opinion mining as the process of analyzing text written in a natural language to classify it as positive, negative, or neutral based on the expressed sentiments. It outlines different levels of opinion mining including document, sentence, and aspect levels. It provides details on the typical architecture of an opinion mining system, including modules for preprocessing, part-of-speech tagging, aspect extraction, opinion identification, and orientation.

1. Shital Katkar (132001005)
VJTI, Mca
13, May, 2016
Review Mining
Sentimental Analysis
Field of Study that analyses peoples
Opinion, Sentiments, attitudes, and
Emotions towards entities such as
products, services, organizations,
Individuals, issues, events.

2. Who needs reviews
 Trend of Online Shopping
 We Don’t know actual material in Hand
 In this case we need a reviews of other people
• Many Production companies need reviews
• To know- what customer likes, what they wants,
their expectations
Will they get reviews
 Reviews are increasing day by day
 Practically impossible to analyse
 Reviews are scattered in natural language
in unstructured data
Automated Opinion Mining approach is needed
Customers
Companies

3. What is Opinion Mining ?
• The process of analysing the text about a topic written in a natural language
• Classify them as Positive, negative or neutral
• Based on the humans sentiments, emotions, opinions expressed in it.
• Due to Growth of Social Media Many users have opportunity to express their opinions
about a product
• These reviews are used by the individuals and organizations for decision making
• It is hard problem
• But its usefulness is increasing day by day.

4. Levels of Opinion Mining
Document Level
Document Level
• Classification Problem
• Input Document should be classified into few
predefined categories
• Opinion Helpfulness Prediction- Helpful or not
• E.g.- Blog Classification , Identifies twitter subject

5. Levels of Opinion Mining
Sentence Level
Document Level
Sentence Level
• Opinion Search and Retrieval sentences are
usually ranked based on certain criteria
• Opinion Summarization
• Classifies the Sentence as positive, negative or
neutral

6. Levels of Opinion Mining
Aspect Level
Sentence Level
Document Level
Aspect Level
• Classifies sentences/documents as positive,
negative or neutral based on the aspects of those
sentences/documents
• Finer grained analysis
• Goal is to discover sentiments on Aspect

7. Levels of Opinion Mining
Aspect Level
Sentence Level
Document Level
Aspect Level
• Core Task – Aspect Identification, Opinion
Identification , Orientation of Opinion towards
aspects
• "The environment is nice but food is bad“
• “The resolution of this camera is nice”
• “This camera is so expensive.”

8. Brief Architecture
Internet
Web Crawler
Review
Collection
OPINION
MINING
SYSTEM
Output Service Calls (API)
Websites Desktop App Mobile App
Excel sheet Analysis

9. Detailed Architecture
Pre-processing
 To improve accuracy
 Avoid unnecessary processing
 Includes
 Unnecessary removal
 Non alphabetical characters
 Smiley removal
Review
Collection
Output
Pre-processor

10. Detailed Architecture
Pre-processing
List<String> UnnecessaryWords=
{“oh”,”OMG”,””,”hello guys”, “thanks”}
Foreach(word in Sentence)
If (word IN UnnecessaryWords)
Then Remove word from Sentence
Review
Collection
Output
Pre-processor

11. Detailed Architecture
POS TaggingReview
Collection
POS Tagging
Output
Pre-processor “Ram is eating”
Ram – Noun
Is – To Be verb (Aux)
Eating – Verb
(NN)
(BE)
(VB)
“Ram/NN is/BE/ eating/VB ”
“Ram/NN is/BE/ eating/VB fast/RB”
“Ram/NN is/BE/ eating/VB chapatti/NN”

12. Detailed Architecture
Review
Collection
POS Tagging
Output
Pre-processor
• NN- Singular Noun
• NNS- Plural Noun
• PN- Pronoun (everything, something)
• RB- Adverb
• VB- Verb
• JJ- Adjective
• WDT- WH Determiner (Which, whom)
• HV - Have
• HV* - Haven’ t

13. Detailed Architecture
POS Tagging (Ambiguity)Review
Collection
POS Tagging
Output
Pre-processor
“The Name of My School/NN is XYZ”
“Ram schooled/VBD in a village”

14. Detailed Architecture
POS Tagging (Ambiguity)Review
Collection
POS Tagging
Output
Pre-processor
“Ram
schooled
In
a
village”
(NN)
(NN/VB)

15. Detailed Architecture
POS Tagging (Ambiguity)Review
Collection
POS Tagging
Output
Pre-processor
“Ram
schooled
In
a
village”
(NN)
(NN/VB)

16. Detailed Architecture
POS Tagging (tools)Review
Collection
POS Tagging
Output
Pre-processor
• MontyLingua
• Berkeley Parser
• QTag
• LB
• OpenNLP
• Lingpipe
• LTAG-Spinal
• FastTag

17. Detailed Architecture
Aspect Extraction
 Aspects – important features
rated by the reviewers
 Identified through the training
process
 Can be single word or a phrase
 Eg.”Service”, “Atmosphere”,
“quality of food “ are aspect of
restaurant
Reviews For
Training
Review
Collection
POS Tagging
Aspect Extraction
Aspect Dictionary
Output
Pre-processor

18. Detailed Architecture
Aspect ExtractionReviews For
Training
Review
Collection
POS Tagging
Aspect Extraction
Aspect Dictionary
Output
Pre-processor
Function Aspect_Extraction(POS_Tagged Sentence)
Foreach(Word in Sentence)
If(Word is NOUN)
Put Word in List -->
ListOfAspects.Add(Word)
Consider Synonymous as Same Word
Count the frequency of each word
Set Minimum Support Count
If aspect count < minimum support count
ListOfAspects.remove (word)

19. Detailed Architecture
Opinion IdentificationReviews For
Training
Review
Collection
POS Tagging
Aspect Extraction
Aspect Dictionary
Output
Pre-processor
Opinion
Identification
• Opinion words are the words which
express opinion towards aspects
• adjectives, verbs, adverb adjective
and adverb verb combinations
• Includes Negation Handling

20. Detailed Architecture
Opinion Word OrientationReviews For
Training
Review
Collection
POS Tagging
Aspect Extraction
Aspect Dictionary
Output
Pre-processor
Opinion
Identification
• Sentimental Word Dictionary
• Includes Negation Handling
Opinion
Orientation
Sentiment words
Dictionary

21. 1. Word that is considered to be positive in one situation may be considered negative in another situation.
Eg. Laptop’s battry is long - +ve
Laptop’s Start Up Time is long - -ve
2. people can be contradictory in their statements. Most reviews will have both positive and negative comments,
which is somewhat manageable by analysing sentences one at a time
Eg. "the movie flopped even though the lead actor rocked it"
“That movie was as good as his last one” (entirely depend upon previous movie)

22. • IEEE lCSC 2015, February 7-9, 2015, Anaheim, California, USA 978-1-4799-7935-6/15/$31.00 ©2015 IEEE, Chinsha T C
And Shibily Joseph , A Syntactic Approach for Aspect Based Opinion Mining
• 978-1- 4788-7225 -8/15/$31.00©2015 IEEE, A.Jeyapriya and C.S.Kanimozhi Selvi, Extracting Aspects and Mining
Opinions in Product Reviews using Supervised Learning Algorithm
• (No. 2009-0075771). Kyung Soo Cho , Na Rae Jung and Ung Mo Kim , Using WordMap and Score-based Weight in
Opinion mining with MapReduce
• http://searchbusinessanalytics.techtarget.com/definition/opinion-mining-sentiment-mining
 Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright c 2014. All rights reserved. Draft of
February 19, 2015.
 Christopher D. Manning and Hinrich Schiitze, Foundations of Statistical Natural Language Processing, The MIT Press
Cambridge, Massachusetts London, England