Transcript Sentiment Analysis

Analysis of sentiment
syntagma using
dependency tree
Serge B. Potemkin
Moscow State University
[email protected]
 Sentiment
◦ A thought, view, or attitude, especially
one based mainly on emotion instead of
reason
 Sentiment
Analysis (opinion mining)
◦ use of natural language processing
(NLP) and computational techniques for
extraction or classification of sentiment
from (unstructured) text
Terms

Consumer information
◦ Product reviews
◦ Consumer attitudes
◦ Trends

Politics
◦ Politicians want to know voters’ views
◦ Voters want to know policitians’ intentions and who else
supports them

Social
Find like-minded individuals or communities

Financial
Predict market trends given the current opinions
What for?

Which features to use?
◦ Words (unigrams)
◦ Phrases/n-grams
◦ Sentences

How to interpret features for sentiment
detection?
◦
◦
◦
◦
Bag of words
Annotated lexicons (WordNet, SentiWordNet)
Syntactic patterns
Paragraph structure
Features
Harder than topical classification, with
which bag of words features perform well
 Must consider other features due to…

◦ Ambiguity of sentiment expression
 irony
 expression of sentiment using neutral words
 … many others
◦ Domain/context dependence
 words/phrases can mean different things in
different contexts and domains
◦ Effect of syntax on semantics
Challenges
Semantic orientation of a sentence
expressed by a ternary predicate:
O(subject, object, sentiment)
 sentiment = {bad, neutral, good}
◦ i.e.,
◦ the subject of assessment considers the
object of assessment to be good or bad
(or neutral = not a sentiment)

Formal description
Predicate O may be expressed explicitly:
(Vania likes Masha) only the surface syntactic analysis is
needed:
Vania (subj) likes (sentiment) Masha (obj)
to determine its semantic orientation (SO).
 The common case is quite different:
(Vania suffers from Masha’s absence) –
both suffer and absence are negative but
the sense is equivalent.

Sentiment expression in NL
Bag of words (number of positive and
negative words) gives good results for
large texts
 Syntagma = a phrase forming a syntactic
unit, say modifier (X) + keyword (Y)
i.e. adjective+noun or adverb+verb
 Signature of syntagma
SO = sgn(X,Y,neg/0/pos).

Bag of words vs. syntagma an.

 X,Y.[sgn(X,Y,pos) 
dep(mod,X,Y),sgn(X,pos),sgn(Y,pos)]. (a)
i.e. if X,Y positive then X+Y positive
  X,Y,Z.[sgn(X,Y,Z) 
dep(mod,X,Y),sgn(X,0),sgn(Y,Z)].
(b)
i.e. if X pos., Y neut. then X+Y pos.
  X,Y,Z.[sgn(X,Y,Z) 
dep(mod,X,Y),sgn(X,Z),sgn(Y,0)].
(c)
SO Calculus
sgn(безумная,радость,pos)=
sgn(mad,happyness,pos),
sgn(бешеный,успех,pos)=
sgn(furious,success,pos),
 sgn(солидный,ущерб,neg)=
sgn(considerable,damage,neg),
sgn(хороший,нагоняй,neg)=
sgn(good,scolding,neg).
 [Kustova, 1]

Different orientation of syntagma
constituent words
sgn(худой,мир,?), sgn(добрая,война,?)
 sgn (bad,peace,?), sgn (good,war,?)
 The expression "a bad peace is better
than a good war," establishes an order
relation "better" among its member
attributive constructions, but one can
assume that both are bad, i.e., sgn
sgn(bad,peace,neg), sgn(good,war,neg).
In some other context, "good war" could
be perceived as a positive phenomenon.

Ambigoues cases
Logical rule of double negation :
*  X,Y,Z.[sgn(X,Y,pos) 
dep(mod,X,Y),sgn(X,neg),sgn(Y,neg)].
fails in NL:
weak opponent, impotent aggressor,
toothless criticism (neut.)
or
bitter sorrow, blatant outrage, brutal
torture (neg.)

Double negative
Methods:
 expert evaluations performed by several
independent experts [Osgood,2], who are
asked to mark up SO of isolated words
and syntagma, assigning them a label
{pos/0/neg}
 corpus techniques, performed on an
sentiment-annotated corpus
[Zagibalov,3],
 SentiWordNet

Syntagma evaluation

Based on WordNet “synsets”
◦ http://wordnet.princeton.edu/

Ternary classifier
◦ Positive, negative, and neutral scores for each
synset

Provides means of gauging sentiment for
a text
SentiWordNet

Created training sets of synsets, Lp and Ln
◦ Start with small number of synsets with
fundamentally positive or negative semantics,
e.g., “nice” and “nasty”
◦ Use WordNet relations, e.g., direct antonymy,
similarity, derived-from, to expand Lp and Ln
over K iterations
◦ Lo (objective) is set of synsets not in Lp or Ln

Trained classifiers on training set
◦ Rocchio and SVM
◦ Use four values of K to create eight classifiers
with different precision/recall characteristics
◦ As K increases, P decreases and R increases
SentiWordNet: Construction

24.6% synsets with Objective<1.0
◦ Many terms are classified with some degree of
subjectivity
10.45% with Objective<=0.5
 0.56% with Objective<=0.125

◦ Only a few terms are classified as definitively
subjective

Difficult (if not impossible) to accurately
assess performance
SentiWordNet: Results

Sentiment annotated corpora (English and
Russian) of approx. 1500 short utterances
concerning popular books. Each utterance
contains from 1 to 15 sentences and was
marked with a label {neg / pos}.
Corpus-based method





- Stemming and determination of
morphological characters of each word
(without morphology disambiguation);
- Parse with obtaining the dependency tree
for each sentence [Potemkin, 4];
- Joining the particle "no/not" to the
associated word (not understand =>
not_understand)
- Selection of constructions modifier+key
word (adjective+noun, adverb+verb);
- Counting the number of occurrences for
each key word = nverb,
Corpus processing
- Counting the number of occurrences in
the positive-marked utterances = nvp and
negatively labeled utterances = nvn
 - Calculation of the normalized
assessment factor for each key word kv =
(nvp-nvn) / nverb;
 - The same calculations for each modifier
to give the normalized assessment factor
kd, and for each syntagma in the corpus the normalized assessment factor ks.

Corpus processing (continued)
Assessment factors ks  [-1,1],
 ks  [-1, -0.6) = neg;
ks  [-0.6, 0.6] = 0;
ks  (0.6, 1] = pos

Assessment thresholds
neg -key
0 -key
pos -key
neg -mod
neg
not_palatable
demagogy
pos –defeated
enemy
neg
uninteresting
book
pos forgotten
kingdoms
neg banal
action-film
pos secondery
pleasure
0 -mod
neg star fever;
pos imminent
defeat;
neg
unexpected
level.
pos only book.
neg. late
success
pos continues
growth
pos -mod
neg happy end
pos fine
rubbish
neg good
intentions
pos pleasant
book
neg sweet
honey
pos best
masterpiece
Table of syntagma signatures
4
2
x 10
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Histogram of syntagma
distribution over the texts
0.8
1
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Histogram of the 1st word of
syntagma distribution
1
6000
5000
4000
3000
2000
1000
0
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Histogram of the 2nd word of
syntagma distribution
1




The report presents considerations for
determining the sentiment of syntagma on
the basis of evaluation of the signature of its
constituent words for structures such as
adjective+noun, verb+adverb.
Logical formulas specifying the calculation of
semantic orientations are listed.
An experiment over the semantically
annotated sentences was performed.
The further research concerning predictive
syntagma of type subject + verb + object will
be undertaken.
Conclusion
http://dict.ruslang.ru/magn.php
 Charles E. Osgood, George Suci, & Percy
Tannenbaum, The Measurement of Meaning.
University of Illinois Press, 1957.
 http://www.informatics.sussex.ac.uk/users
/tz21/
 http://sunsite.informatik.rwthaachen.de/Publications/CEUR-WS/Vol476/paper6.pdf

References