Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.

1. Sentiment Analysis
A descriptive approach towards its Classi

2. cation
Techniques
Sangeeth Nagarajan
MR1-CSE Roll No :13
Guided By
Asst Prof: Rejimoan R
July 29, 2014
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3. Content
1 Introdction
2 Sentiment Analysis
3 Related Works
4 General Procedure
5 Data Preparation And Feature Extraction
6 Fuzzy Control System for Sentiment Analysis
7 Neuro Fuzzy Inference System for Sentiment Analysis
8 Hidden Markov Model for Sentiment Analysis
9 Hybrid Structure
10 Conclusion
11 References
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4. Case Study 1
In late 1980s, a person is planning to buy a black and television . What
can be done to verify the quality and performance of the system?
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5. Case Study 1
Solutions:
He can check with the person who were using the system
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6. Case Study 1
Solutions:
He can check with the person who were using the system
He can directly discuss with customer care person.
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7. Case Study 2
Imagine you want to buy a smartphone with latest features. What will you
don to know the features provided by dierent companies?
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8. Case Study 2
Solutions:
He can check with the person who were using the system
He can directly discuss with customer care person.
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9. Case Study 2
Solutions:
He can check with the person who were using the system
He can directly discuss with customer care person.
Added to these, you can surf website that provide compartive features
of smartphone and Users Review
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10. Sentiment Analysis
Process of determining the overall rating of a commodity from users
review is called Sentiment Analysis
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11. Introduction I
Process used to determine the attitude/ opinion/ emotion expressed
by a person about a particular topic.
Uses natural language processing and text analytics to identify and
extract subjective information in source materials.
Automatically characterize the overall feeling or mood of consumers
toward a speci

12. c brand or company and determine whether they are
viewed positively or negatively.
Companies and organizations are interested in

13. nding out customers
opinions about products and services via social media.
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14. Introduction II
The main Goal is for :
Detecting whether a segment of text contains an expression of
opinion.
Detecting the overall polarity of the text :- positive or negative.
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15. Sentiment Analysis I
Sentiment Analysis have many other name
1 Opinion Extraction
2 Opinion Mining
3 Sentiment Mining
4 Subjective Analysis
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16. Sentiment Analysis II
Why Sentiment Analysis?
Movie : Is this review postive or negative?
Products: What do people think about the new iPhone?
Politics: What do people think about the candidate or issue?
Prediction : Predict emotion outcomes or market trends from
sentiment.
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17. Related Works I
1 Learning Methods: The dierent learning types are as follows
Supervised learning: Learning classi

18. er from training data and assign
class labels to test data.
Unsupervised learning: Learning without training data.
Semi-supervised learning: Amalgamate both labeled and unlabeled
training data.
2 Classi

19. cation methods: There are Natural Language Processing and
pattern-based, machine learning algorithms,such as
Naive Bayes (NB)
Maximum Entropy (ME)
Support Vector Machines (SVM)
Fuzzy Interface System Class

20. cation
Neural Fuzzy Interface System Class

21. cation
Hidden Markov Model
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22. Related Works II
3 Feature Extaction Methods: There are four feature categories feature
extraction methods used in sentiment analysis studies. These include
Syntactic Feature
Semantic Feature
Link-Based Feature
Stylistic Feature
Based On Occurences of Word in Corpus
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23. General Procedure I
Figure : Steps And Techniques used in Sentiment Analysis
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24. General Procedure II
1 Text Preprocessing:- Divided into two subcategories.
Tokenization:- The documents are separated as tokens and used for
further processing.
Removal of Stop Words:- Some of the more frequently used stop words
for English include a, of, the, I, it, you, and and these
are generally regarded as 'functional words' which do not carry
meaning. It is practical to remove those words which appear too often.
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25. General Procedure III
2 Text Transformation
The score of each sentence is calculated by sum of weight of each term
in the corresponding sentences.
The weight of each term is calculated by multiplication of TF and IDF
of that word based on adjective word extracted from Parts of speech
tags.
TF(t) =
P
N
(1)
where,
P=Number of times the adjective term occurs in document(d)
N=Total Number of adjective in document (d).
IDF (t) = log
ND
DF(t)
(2)
ND = total number of document in the document collection
DF (t) = number of documents in which adjective term (t)
occurs in the document collection.
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26. General Procedure IV
3 Feature Selection
The simplest statistical approach for feature selection is to use the
most frequently occurring words in the corpus as polarity indicators.
The majority of the approaches for sentiment analysis involve a
two-step process:
i. Identify the parts of the document to contribute the positive or
negative sentiments.
ii. Join these parts of the document in ways that increase the odds of the
document falling into one of these two polar categories.
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27. General Procedure V
4 Sentiment Classi

28. cation
Classi

29. cation of sentences into postive, negative and neutral polarity.
Not a clear boundary between the concepts of positive,negative
and neutral.
We can use fuzzy set classi

30. cation or machine learning methods for
this process.
In fuzzy set, membership function for each set is de

31. ned and options
havings highest membership function is allocated to the group set.
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32. General Procedure VI
5 Parameters for Evaluation
Table : Contegency Table
Correct labels
Positive Negative
Classi

33. ed
labels
Positive TP(True Positive) FP(False Positive)
Negative FN(False Negative) TN(True Negative)
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34. General Procedure VII
Accuracy =
TP + TN
TP + TN + FP + FN
(3)
Precision =
TP
TP + FP
(4)
Recall =
TP
TP + FN
(5)
F =
2 Precision Recall
Precision + Recall
(6)
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35. Data Preparation And Feature Extraction I
Feature Extraction algorithm consists of two parts:
i. Data Preparation.
ii. Calculation of Feature Vectors.
Data Preparation
Use a sentiment polarity dataset v2.0.
Machine learning based Classi

36. cation consist of two set
1) Training set: Used by an automatic classi

37. er to learn the dierentiating
characteristics of documents.
2) Test set : Validate the performance of the automatic classi

38. er.
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39. Data Preparation And Feature Extraction II
Operations carried out are as follows:
* Combine all

40. les from the corpus and make one text

41. le.
* Convert the text to an array of words.
* Sort the array of words ascending order.
* Code:V = fv1; :::; vMg, where M is the number of dierent words
(terms) in the corpusCombine all

42. les from the corpus and make one
text

43. le.
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44. Data Preparation And Feature Extraction III
Calculation of Feature Vectors
r2
r1
N is the number of classes.
M is the number of dierent words (terms) in the corpus.
R is the number of observed sequences in the training process.
W = fw;w; ::;wrT
r
g are the reviews in the training dataset, where
Tr is the length of r-th review, r = 1; 2; :::; R.
i ;j describes the association between i-th term (word) and the j-th
class.
ci ;j is the number of times i-th term occurred in the j-th class.
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45. Data Preparation And Feature Extraction IV
ei is the normalized entropy of the i-th term in the corpus
ei = 1
lgN
NP
j=1
( ci ;j
ti
lg ci ;j
ti
), i = 1; :::;M;j = 1; :::;N.
ti =
P
j
ci ;j denotes the occurrence times of the i-th term in the
corpus.
Calucate Membership degree of term by an analytical formula:
i ;j =
8
:
ci ;j
MP
v=1
cv;j
(1ei )
NP
f
t=1
MP
v=1
cv;j (1ei )g
; ni nmin
0; ni nmin
(7)
where nmin = 40
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46. Fuzzy Control System for Sentiment Analysis I
Fuzzy inference is the process of formulating the mapping from given
input(s) to output(s) using fuzzy logic.
The process involves membership functions, logic operations, and
if-then rules.
At

47. rst stage membership function is estimated, then apply fuzzy
operations and modify parameters by the back-propagation
algorithm.
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48. Fuzzy Control System for Sentiment Analysis II
Figure : Realization scheme of fuzzy control process
1 The membership degree of terms (ri
;j ) of the r -th review are
calculated by (7).
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49. Fuzzy Control System for Sentiment Analysis III
2 Maximum membership degree is found with respect to the classes for
every term of the r-th sentiment
ri
;j = ri
;j ; j = arg ( max
1vN
ri
;v ); i = 1; ::;M: (8)
3 Means of maxima are calculated for all classes:
r
j =
P
k2Zr
j
r
k;j
l r
j
; Zr
j = fi : ri
;v ) = max
1vN
ri
;v g; j = 1; ::;N: (9)
where l r
j = jZr
j j is the number of elements of the set Zr
j
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50. Fuzzy Control System for Sentiment Analysis IV
Use the centre of gravity defuzzi

51. cation method for defuzzi

52. cation
operation.
It avoids ambiguities which may arise when an output degree of
membership comes from more than one crisp output value.
The objective function is de

53. ned as follows:
E(y) =
1
2
XR
r=1
(
r
j yj
NP
j=1
r
j
dr )2 ! min
y2RN
; (10)
y = y1; y2; ::; yN; dr 2 f1; 2; ::;Ng
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54. Fuzzy Control System for Sentiment Analysis V
The partial derivatives of this function are calculated in following
form:
@E(y)
@yt
=
XR
r=1
r
j
NP
j=1
r
j
(
NP
r
j yj
NP
j=1
j=1
r
j
dr ); t = 1; 2; ::N: (11)
Rounding of y shows the index of the classes obtained in the result
y =
NP
j=1
jy
j
NP
j=1
j
(12)
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55. Fuzzy Control System for Sentiment Analysis VI
Acceptance strategy (s):
s =
(
is 2 I ; if y 2 (is 41; is +41)
reject; otherwise
(13)
where is is the index of the appropriate class, I =1,2,...,N.Here
41 2 [0; 0:5) is the main quantity, which in
uences the reliability of
the system. Results of sentiment analysis of movie reviews with
dierent values of 41 in Table 2 is as shown.
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56. Fuzzy Control System for Sentiment Analysis VII
Table : Result of FCS for Classi

57. cation of movie reviews
Folds
41 = 0:4 42 = 0:45 No
Rejection
Corr(%) Rej(%) Err(%) Corr(%) Rej(%) Err(%) Correct(%)
1 65 24.5 10.5 74.5 12 13.5 81
2 73.5 19 7.5 79.5 9 11.5 84
3 66 24.5 9.5 73.5 12.5 14 81
4 71.5 22 6.5 77 11.5 11.5 83.5
5 72.5 19.5 8 81 7.5 11.5 84.5
6 70 19.5 10.5 77.5 7.5 15 81.5
7 70.5 19 10.5 76.5 9 14.5 81
8 69 22 9 75.5 11.5 13 82
9 66 24 10 73 12 15 81.5
10 71.5 19.5 9 80 7.5 12.5 84.5
Averg 69.55 21.35 9.1 76.8 10 13.2 82.45
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58. Neuro Fuzzy Inference System for Sentiment Analysis I
Fuzzy logic systems are good at explaining their decisions, but they
cannot automatically acquire the rules they use to make those
decisions.
Neural networks are good at recognizing patterns, they are not good
at explaining how they reach their decisions.
Creation of intelligent hybrid systems where two or more techniques
are combined in an appropriate manner can overcome the limitations
of individual techniques
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59. Neuro Fuzzy Inference System for Sentiment Analysis II
Figure : The Structure of ANFIS
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60. Neuro Fuzzy Inference System for Sentiment Analysis III
Table : Results of ANFIS for Classi

61. cation of movie reviews
Folds
42 = 0:5;43 = 0:5 42 = 0:1;43 = 0:5 No
Rejection
Corr(%) Rej(%) Err(%) Corr(%) Rej(%) Err(%) Correct(%)
1 63.5 26.5 10 73.5 12.5 14 81
2 68 26 6 79 10 11 85.5
3 65 27 8 72.5 15.5 12 81
4 70.5 23.5 6 77 11 12 835
5 64 29.5 6.5 80 9 11 86
6 69 21 10 76 10 14 82.5
7 70 21 9 77 8 15 81.5
8 65.5 26 8.5 75 12 13 82.5
9 66 22.5 11.5 73.5 13 13.5 81
10 68.5 23 8.5 80 7.5 12.5 85
Averg 67 24 8.4 76.35 10.85 12.8 83
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62. Hidden Markov Model For Sentiment Analysis I
Bayes' theorem
Bayes' theorem gives the relationship between the probabilities of A
and B, P(A) and P(B), and the conditional probabilities of A given B
and B given A, P(AjB) and P(BjA) . In its most common form, it is:
P(AjB) =
P(BjA) P(B)
P(A)
(14)
Helps to use a known outcome to predict the sequence of events
leading up to that outcome.
Example
We need to know which party is ruling based on tax cut.
Let there be two party A and B.
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63. Hidden Markov Model For Sentiment Analysis II
We know since there is two parties, probability of each party to rule is
0.5. i.e.
* P(A) = 0.5
* P(B) = 0.5
From the previous history we can get the details of probability of tax
cut given party A or B was elected. Let
* P(tjA) = 0.25, then P(t0jA)= 0.75
* P(tjB) = 0.85, then P(t0jB)= 0.15
Figure : Tree Model
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64. Hidden Markov Model For Sentiment Analysis III
From the tree model, we can calculate the probability of P(t).
P(t) = P(tjA) P(A) + P(tjB) P(B)
= (0:25 0:5) + (0:85 0:5)
= 0:125 + 0:425
= 0:55
Therefore by Bayes' Theorem,
P(Bjt) = P(tjB) P(B)=P(t)
= (0:85 0:5)=0:55
= 0:772
P(Ajt) = P(tjA) P(A)=P(t)
= (0:25 0:5)=0:55
= 0:227
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65. Hidden Markov Model For Sentiment Analysis IV
Markov Model
Figure : Markov Model
3 states- Bull,Bear and Even
3 observations- Up, Down and Unchanged.
For given a sequence of observations,up-down-down,
the state sequences is Bull-Bear-Bear
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66. Hidden Markov Model For Sentiment Analysis V
Figure : Hidden Markov Model
The key dierence is that if we have the observation sequence,
up-down-down, then we cannot say exactly what state sequence
produced and thus the state sequence is hidden.
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67. Hidden Markov Model For Sentiment Analysis VI
Calculate the probability that the model produced the sequence, as
well as which state sequence was most likely to have produced the
observations.
Applied in many areas of signal processing, and in particular speech
processing. Also been applied with success to low level NLP tasks
such as part-of-speech tagging, phrase chunking, and extracting
target information from documents.
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68. Hidden Markov Model For Sentiment Analysis VII
The parameters of the HMM applied in the system as follows
1 N is the number of states.
2 M is the number of dierent words (terms) of reviews taking part in
the training process for the given problem.
3 V includes all possible observations sets, V = fv1; :::; vMg (The
elements of these sets in the understanding problem are dierent words
than are in the reviews taking part in the training process)
4 = figi = 1N are initial state distributions: i = P(q1 = i)
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69. Hidden Markov Model For Sentiment Analysis VIII
5 A = bai ;j c is the state transition probability matrix,
ai ;j = P(qt = 1jqj = i); 1 i ; j N.
6 B = fbj (ot )gNj
=1 are the state-dependent observation probabilities.
Here, for every state j, bj (ot ) = P(ot jqt = j) is the probability
distribution of words occurring in states.
7 O(r ) = [o(r )
1 ; o(r )
2 ; :::; o(r )
T ] are the observation sequences, where R is the
number of observed sequences, Tr is the length of r-th observed
sequence, Tr T , T is the given quantity, r=1,2 ,...,R.
The parameters of the HMM are estimated according to each of the
corresponding classes and trained by the Baum-Welch algorithm.
The calculated probabilities are passed to a decision-making block.
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70. Hidden Markov Model For Sentiment Analysis IX
Table : Results of HMM For Polarity Reviews
Folds 1 state 2 state 3 state 5 state
1 79.5 81 84 78
2 83.5 83.5 82 82
3 77.5 81.5 81 79.5
4 80.5 82.5 84.5 81
5 84.5 83 86.5 81
6 82.5 82.5 83.5 81
7 82.5 83 82 80
8 83.5 84.5 84.5 84
9 77 79 78.5 77
10 83.5 83.5 83 83
Averg 81.45 82.4 82.95 80.65
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71. Hybrid Structure I
Hybrid-I. This system con

72. rms the results veri

73. ed by the FCS, ANFIS
and HMM approaches. If some of these models discard understanding,
then the system does not accept any decision. This system prevents
the error in the understanding process and therefore is more reliable.
Table : Results of Hybrid-I
Folds
FCS(%) ANFIS(%) HMM-3(%) Hybrid- I
Corr(%) Rej(%) Err(%)
1 81 81.5 84 75.5 14 10.5
2 84 85.5 82 77.5 12 10.5
3 81 81 81 74.5 12.5 13
4 83.5 83.5 84.5 76.5 15 8.5
5 84.5 86 86.5 80 11 9
6 81.5 82.5 83.5 78 9 13
7 81 81.5 82 74.5 14 11.5
8 82 82.5 84.5 78.5 10 11.5
9 81.5 81 78.5 74 12 14
10 84.5 85 83 80 7.5 12.5
Averg 82.45 83 82.95 76.9 11.7 11.4
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74. Hybrid Structure II
Hybrid-II. The method suggest in this system is a sequential method.
The procedure is that if one classi

75. er fails to classify a document, the
classi

76. er will pass the document onto the next classi

77. er, until the
document is classi

78. ed or no other classi

79. er exists. This approach
minimizes the number of rejected reviews.
Table : Results of Hybrid-II
Folds
ANFIS(%) HMM-3(%) Hybrid- II(%)
Corr(%) Rej(%) Err(%)
1 60 32 8 84 84
2 61 35 4 82 84
3 60.5 34.5 5 81 81.5
4 66.5 30 3.5 84.5 87
5 58 37 5 86.5 87.5
6 66 26 8 83.5 84
7 63.5 29.5 7 82 83
8 63.5 30 6.5 84.5 84.5
9 61 30 9 78.5 79.5
10 63 30 7 83 84.5
Averg 62.3 31.4 6.3 82.95 83.95
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80. Questions
Sangeeth Nagarajan (SCTEC) Sentiment Analysis July 29, 2014 46 / 54

81. Conclusion
Sentiment analysis is the process used to determine the
attitude/opinion/emotion expressed by a person about a particular
topic.
Sentiment analysis or opinion mining uses natural language processing
and text analytics to identify and extract subjective information in
source materials.
Several Machine learning algorithms can be used for classi

82. cation of
document-level sentence:-SVM, NB, ME, HMM, FCS and ANFIS
The combination of multiple classi

83. ers can result in better accuracy
than that achieved by any individual classi

84. er.
Sangeeth Nagarajan (SCTEC) Sentiment Analysis July 29, 2014 47 / 54

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