Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X),
pages 109–116, New York City, June 2006. c©2006 Association for Computational Linguistics
Which Side are You on? Identifying Perspectives at the Document and
Sentence Levels
Wei-Hao Lin
Language Technologies Institute
Carnegie Mellon University
Pittsburgh, PA 15213
whlin@cs.cmu.edu
Theresa Wilson, Janyce Wiebe
Intelligent Systems Program
University of Pittsburgh
Pittsburgh, PA 15260
{twilson,wiebe}@cs.pitt.edu
Alexander Hauptmann
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
alex@cs.cmu.edu
Abstract
In this paper we investigate a new problem
of identifying the perspective from which
a document is written. By perspective we
mean a point of view, for example, from
the perspective of Democrats or Repub-
licans. Can computers learn to identify
the perspective of a document? Not every
sentence is written strongly from a per-
spective. Can computers learn to identify
which sentences strongly convey a partic-
ular perspective? We develop statistical
models to capture how perspectives are
expressed at the document and sentence
levels, and evaluate the proposed mod-
els on articles about the Israeli-Palestinian
conflict. The results show that the pro-
posed models successfully learn how per-
spectives are reflected in word usage and
can identify the perspective of a document
with high accuracy.
1 Introduction
In this paper we investigate a new problem of au-
tomatically identifying the perspective from which
a document is written. By perspective we mean
a “subjective evaluation of relative significance, a
point-of-view.”1 For example, documents about the
Palestinian-Israeli conflict may appear to be about
the same topic but reveal different perspectives:
1The American Heritage Dictionary of the English Lan-
guage, 4th ed.
(1) The inadvertent killing by Israeli forces of
Palestinian civilians – usually in the course of
shooting at Palestinian terrorists – is
considered no different at the moral and ethical
level than the deliberate targeting of Israeli
civilians by Palestinian suicide bombers.
(2) In the first weeks of the Intifada, for example,
Palestinian public protests and civilian
demonstrations were answered brutally by
Israel, which killed tens of unarmed protesters.
Example 1 is written from an Israeli perspective;
Example 2 is written from a Palestinian perspec-
tive. Anyone knowledgeable about the issues of
the Israeli-Palestinian conflict can easily identify the
perspectives from which the above examples were
written. However, can computers learn to identify
the perspective of a document given a training cor-
pus?
When an issue is discussed from different per-
spectives, not every sentence strongly reflects the
perspective of the author. For example, the follow-
ing sentences were written by a Palestinian and an
Israeli.
(3) The Rhodes agreements of 1949 set them as
the ceasefire lines between Israel and the Arab
states.
(4) The green line was drawn up at the Rhodes
Armistice talks in 1948-49.
Examples 3 and 4 both factually introduce the back-
ground of the issue of the “green line” without ex-
pressing explicit perspectives. Can we develop a
109
system to automatically discriminate between sen-
tences that strongly indicate a perspective and sen-
tences that only reflect shared background informa-
tion?
A system that can automatically identify the per-
spective from which a document is written will be
a valuable tool for people analyzing huge collec-
tions of documents from different perspectives. Po-
litical analysts regularly monitor the positions that
countries take on international and domestic issues.
Media analysts frequently survey broadcast news,
newspapers, and weblogs for differing viewpoints.
Without the assistance of computers, analysts have
no choice but to read each document in order to iden-
tify those from a perspective of interest, which is ex-
tremely time-consuming. What these analysts need
is to find strong statements from different perspec-
tives and to ignore statements that reflect little or no
perspective.
In this paper we approach the problem of learning
individual perspectives in a statistical framework.
We develop statistical models to learn how perspec-
tives are reflected in word usage, and we treat the
problem of identifying perspectives as a classifica-
tion task. Although our corpus contains document-
level perspective annotations, it lacks sentence-level
annotations, creating a challenge for learning the
perspective of sentences. We propose a novel sta-
tistical model to overcome this problem. The ex-
perimental results show that the proposed statisti-
cal models can successfully identify the perspective
from which a document is written with high accu-
racy.
2 Related Work
Identifying the perspective from which a document
is written is a subtask in the growing area of au-
tomatic opinion recognition and extraction. Sub-
jective language is used to express opinions, emo-
tions, and sentiments. So far, research in automatic
opinion recognition has primarily addressed learn-
ing subjective language (Wiebe et al., 2004; Riloff
et al., 2003), identifying opinionated documents (Yu
and Hatzivassiloglou, 2003) and sentences (Yu and
Hatzivassiloglou, 2003; Riloff et al., 2003), and dis-
criminating between positive and negative language
(Pang et al., 2002; Morinaga et al., 2002; Yu and
Hatzivassiloglou, 2003; Turney and Littman, 2003;
Dave et al., 2003; Nasukawa and Yi, 2003; Popescu
and Etzioni, 2005; Wilson et al., 2005). While by its
very nature we expect much of the language that is
used when presenting a perspective or point-of-view
to be subjective, labeling a document or a sentence
as subjective is not enough to identify the perspec-
tive from which it is written. Moreover, the ideol-
ogy and beliefs authors possess are often expressed
in ways other than positive or negative language to-
ward specific targets.
Research on the automatic classification of movie
or product reviews as positive or negative (e.g.,
(Pang et al., 2002; Morinaga et al., 2002; Turney
and Littman, 2003; Nasukawa and Yi, 2003; Mullen
and Collier, 2004; Beineke et al., 2004; Hu and Liu,
2004)) is perhaps the most similar to our work. As
with review classification, we treat perspective iden-
tification as a document-level classification task, dis-
criminating, in a sense, between different types of
opinions. However, there is a key difference. A pos-
itive or negative opinion toward a particular movie
or product is fundamentally different from an overall
perspective. One’s opinion will change from movie
to movie, whereas one’s perspective can be seen as
more static, often underpinned by one’s ideology or
beliefs about the world.
There has been research in discourse analysis that
examines how different perspectives are expressed
in political discourse (van Dijk, 1988; Pan et al.,
1999; Geis, 1987). Although their research may
have some similar goals, they do not take a compu-
tational approach to analyzing large collections of
documents. To the best of our knowledge, our ap-
proach to automatically identifying perspectives in
discourse is unique.
3 Corpus
Our corpus consists of articles published on the
bitterlemonswebsite2. The website is set up to
“contribute to mutual understanding [between Pales-
tinians and Israelis] through the open exchange of
ideas.”3 Every week an issue about the Israeli-
Palestinian conflict is selected for discussion (e.g.,
2http://www.bitterlemons.org
3http://www.bitterlemons.org/about/
about.html
110
“Disengagement: unilateral or coordinated?”), and
a Palestinian editor and an Israeli editor each con-
tribute one article addressing the issue. In addition,
the Israeli and Palestinian editors invite one Israeli
and one Palestinian to express their views on the
issue (sometimes in the form of an interview), re-
sulting in a total of four articles in a weekly edi-
tion. We choose the bitterlemons website for
two reasons. First, each article is already labeled
as either Palestinian or Israeli by the editors, allow-
ing us to exploit existing annotations. Second, the
bitterlemons corpus enables us to test the gen-
eralizability of the proposed models in a very real-
istic setting: training on articles written by a small
number of writers (two editors) and testing on arti-
cles from a much larger group of writers (more than
200 different guests).
We collected a total of 594 articles published on
the website from late 2001 to early 2005. The dis-
tribution of documents and sentences are listed in
Table 1. We removed metadata from all articles, in-
Palestinian Israeli
Written by editors 148 149
Written by guests 149 148
Total number of documents 297 297
Average document length 740.4 816.1
Number of sentences 8963 9640
Table 1: The basic statistics of the corpus
cluding edition numbers, publication dates, topics,
titles, author names and biographic information. We
used OpenNLP Tools4 to automatically extract sen-
tence boundaries, and reduced word variants using
the Porter stemming algorithm.
We evaluated the subjectivity of each sentence us-
ing the automatic subjective sentence classifier from
(Riloff and Wiebe, 2003), and find that 65.6% of
Palestinian sentences and 66.2% of Israeli sentences
are classified as subjective. The high but almost
equivalent percentages of subjective sentences in the
two perspectives support our observation in Sec-
tion 2 that a perspective is largely expressed using
subjective language, but that the amount of subjec-
tivity in a document is not necessarily indicative of
4http://sourceforge.net/projects/
opennlp/
its perspective.
4 Statistical Modeling of Perspectives
We develop algorithms for learning perspectives us-
ing a statistical framework. Denote a training corpus
as a set of documents Wn and their perspectives la-
bels Dn, n = 1,...,N, where N is the total number
of documents in the corpus. Given a new document
˜W with a unknown document perspective, the per-
spective ˜D is calculated based on the following con-
ditional probability.
P( ˜D| ˜W,{Dn,Wn}Nn=1) (5)
We are also interested in how strongly each sen-
tence in a document conveys perspective informa-
tion. Denote the intensity of the m-th sentence of
the n-th document as a binary random variable Sm,n.
To evaluate Sm,n, how strongly a sentence reflects
a particular perspective, we calculate the following
conditional probability.
P(Sm,n|{Dn,Wn}Nn=1) (6)
4.1 Na¨ıve Bayes Model
We model the process of generating documents from
a particular perspective as follows:
pi ∼ Beta(αpi,βpi)
θ ∼ Dirichlet(αθ)
Dn ∼ Binomial(1,pi)
Wn ∼ Multinomial(Ln,θd)
First, the parameters pi and θ are sampled once from
prior distributions for the whole corpus. Beta and
Dirichlet are chosen because they are conjugate pri-
ors for binomial and multinomial distributions, re-
spectively. We set the hyperparameters αpi,βpi, and
αθ to one, resulting in non-informative priors. A
document perspective Dn is then sampled from a bi-
nomial distribution with the parameter pi. The value
of Dn is either d0 (Israeli) or d1 (Palestinian). Words
in the document are then sampled from a multino-
mial distribution, where Ln is the length of the doc-
ument. A graphical representation of the model is
shown in Figure 1.
111
pi θ
Dn Wn
N
Figure 1: Na¨ıve Bayes Model
The model described above is commonly known
as a na¨ıve Bayes (NB) model. NB models have
been widely used for various classification tasks,
including text categorization (Lewis, 1998). The
NB model is also a building block for the model
described later that incorporates sentence-level per-
spective information.
To predict the perspective of an unseen document
using na¨ıve Bayes , we calculate the posterior distri-
bution of ˜D in (5) by integrating out the parameters,
integraldisplay integraldisplay
P( ˜D,pi,θ|{(Dn,Wn)}Nn=1, ˜W)dpidθ (7)
However, the above integral is difficult to compute.
As an alternative, we use Markov Chain Monte
Carlo (MCMC) methods to obtain samples from the
posterior distribution. Details about MCMC meth-
ods can be found in Appendix A.
4.2 Latent Sentence Perspective Model
We introduce a new binary random variable, S, to
model how strongly a perspective is reflected at the
sentence level. The value of S is either s1 or s0,
where s1 indicates a sentence is written strongly
from a perspective while s0 indicates it is not. The
whole generative process is modeled as follows:
pi ∼ Beta(αpi,βpi)
τ ∼ Beta(ατ,βτ)
θ ∼ Dirichlet(αθ)
Dn ∼ Binomial(1,pi)
Sm,n ∼ Binomial(1,τ)
Wm,n ∼ Multinomial(Lm,n,θ)
The parameters pi and θ have the same semantics as
in the na¨ıve Bayes model. S is naturally modeled as
a binomial variable, where τ is the parameter of S.
S represents how likely it is that a sentence strongly
conveys a perspective. We call this model the La-
tent Sentence Perspective Model (LSPM) because S
is not directly observed. The graphical model repre-
sentation of LSPM is shown in Figure 2.
pi τ θ
Dn
Sm,n Wm,n
N
Mn
Figure 2: Latent Sentence Perspective Model
To use LSPM to identify the perspective of a new
document ˜D with unknown sentence perspectives ˜S,
we calculate posterior probabilities by summing out
possible combinations of sentence perspective in the
document and parameters.
integraldisplay integraldisplay integraldisplay summationdisplay
Sm,n
summationdisplay
˜S
P( ˜D,Sm,n, ˜S,pi,τ,θ| (8)
{(Dn,Wn)}Nn=1, ˜W)dpidτdθ
As before, we resort to MCMC methods to sample
from the posterior distributions, given in Equations
(5) and (6).
As is often encountered in mixture models, there
is an identifiability issue in LSPM. Because the val-
ues of S can be permuted without changing the like-
lihood function, the meanings of s0 and s1 are am-
biguous. In Figure 3a, four θ values are used to rep-
resent the four possible combinations of document
perspective d and sentence perspective intensity s. If
we do not impose any constraints, s1 and s0 are ex-
changeable, and we can no longer strictly interpret
s1 as indicating a strong sentence-level perspective
and s0 as indicating that a sentence carries little or
no perspective information. The other problem of
this parameterization is that any improvement from
LSPM over the na¨ıve Bayes model is not necessarily
112
d0
θd0,s0
s0
θd0,s1
s1
d1
θd1,s0
s0
θd0,s0
s1
(a) s0 and s1 are not identifiable
s1
θd0,s1
d0
θd1,s1
d1 θs0
s0
(b) sharing θd1,s0 and
θd0,s0
Figure 3: Two different parameterization of θ
due to the explicit modeling of sentence-level per-
spective. S may capture aspects of the document
collection that we never intended to model. For ex-
ample, s0 may capture the editors’ writing styles and
s1 the guests’ writing styles in the bitterlemons
corpus.
We solve the identifiability problem by forcing
θd1,s0 and θd0,s0 to be identical and reducing the
number of θ parameters to three. As shown in Fig-
ure 3b, there are separate θ parameters conditioned
on the document perspective (left branch of the tree,
d0 is Israeli and d1 is Palestinian), but there is single
θ parameter when S = s0 shared by both document-
level perspectives (right branch of the tree). We as-
sume that the sentences with little or no perspective
information, i.e., S = s0, are generated indepen-
dently of the perspective of a document. In other
words, sentences that are presenting common back-
ground information or introducing an issue and that
do not strongly convey any perspective should look
similar whether they are in Palestinian or Israeli doc-
uments. By forcing this constraint, we become more
confident that s0 represents sentences of little per-
spectives and s1 represents sentences of strong per-
spectives from d1 and d0 documents.
5 Experiments
5.1 Identifying Perspective at the Document
Level
We evaluate three different models for the task
of identifying perspective at the document level:
two na¨ıve Bayes models (NB) with different infer-
ence methods and Support Vector Machines (SVM)
(Cristianini and Shawe-Taylor, 2000). NB-B uses
full Bayesian inference and NB-M uses Maximum
a posteriori (MAP). We compare NB with SVM not
only because SVM has been very effective for clas-
sifying topical documents (Joachims, 1998), but also
to contrast generative models like NB with discrimi-
native models like SVM. For training SVM, we rep-
resent each document as a V -dimensional feature
vector, where V is the vocabulary size and each co-
ordinate is the normalized term frequency within the
document. We use a linear kernel for SVM and
search for the best parameters using grid methods.
To evaluate the statistical models, we train them
on the documents in the bitterlemons corpus
and calculate how accurately each model predicts
document perspective in ten-fold cross-validation
experiments. Table 2 reports the average classi-
fication accuracy across the the 10 folds for each
model. The accuracy of a baseline classifier, which
randomly assigns the perspective of a document as
Palestinian or Israeli, is 0.5, because there are equiv-
alent numbers of documents from the two perspec-
tives.
Model Data Set Accuracy Reduction
Baseline 0.5
SVM Editors 0.9724
NB-M Editors 0.9895 61%
NB-B Editors 0.9909 67%
SVM Guests 0.8621
NB-M Guests 0.8789 12%
NB-B Guests 0.8859 17%
Table 2: Results for Identifying Perspectives at the
Document Level
The last column of Table 2 is error reduction
relative to SVM. The results show that the na¨ıve
Bayes models and SVM perform surprisingly well
on both the Editors and Guests subsets of the
bitterlemons corpus. The na¨ıve Bayes mod-
els perform slightly better than SVM, possibly be-
cause generative models (i.e., na¨ıve Bayes models)
achieve optimal performance with a smaller num-
ber of training examples than discriminative models
(i.e., SVM) (Ng and Jordan, 2002), and the size of
the bitterlemonscorpus is indeed small. NB-B,
which performs full Bayesian inference, improves
113
on NB-M, which only performs point estimation.
The results suggest that the choice of words made
by the authors, either consciously or subconsciously,
reflects much of their political perspectives. Statis-
tical models can capture word usage well and can
identify the perspective of documents with high ac-
curacy.
Given the performance gap between Editors and
Guests, one may argue that there exist distinct edit-
ing artifacts or writing styles of the editors and
guests, and that the statistical models are capturing
these things rather than “perspectives.” To test if the
statistical models truly are learning perspectives, we
conduct experiments in which the training and test-
ing data are mismatched, i.e., from different subsets
of the corpus. If what the SVM and na¨ıve Bayes
models learn are writing styles or editing artifacts,
the classification performance under the mismatched
conditions will be considerably degraded.
Model Training Testing Accuracy
Baseline 0.5
SVM Guests Editors 0.8822
NB-M Guests Editors 0.9327 43%
NB-B Guests Editors 0.9346 44%
SVM Editors Guests 0.8148
NB-M Editors Guests 0.8485 18%
NB-B Editors Guests 0.8585 24%
Table 3: Identifying Document-Level Perspectives
with Different Training and Testing Sets
The results on the mismatched training and test-
ing experiments are shown in Table 3. Both SVM
and the two variants of na¨ıve Bayes perform well
on the different combinations of training and testing
data. As in Table 2, the na¨ıve Bayes models per-
form better than SVM with larger error reductions,
and NB-B slightly outperforms NB-M. The high ac-
curacy on the mismatched experiments suggests that
statistical models are not learning writing styles or
editing artifacts. This reaffirms that document per-
spective is reflected in the words that are chosen by
the writers.
We list the most frequent words (excluding stop-
words) learned by the the NB-M model in Ta-
ble 4. The frequent words overlap greatly be-
tween the Palestinian and Israeli perspectives, in-
cluding “state,” “peace,” “process,” “secure” (“se-
curity”), and “govern” (“government”). This is in
contrast to what we expect from topical text classi-
fication (e.g., “Sports” vs. “Politics”), in which fre-
quent words seldom overlap. Authors from differ-
ent perspectives often choose words from a simi-
lar vocabulary but emphasize them differently. For
example, in documents that are written from the
Palestinian perspective, the word “palestinian” is
mentioned more frequently than the word “israel.”
It is, however, the reverse for documents that are
written from the Israeli perspective. Perspectives
are also expressed in how frequently certain people
(“sharon” v.s. “arafat”), countries (“international”
v.s. “america”), and actions (“occupation” v.s. “set-
tle”) are mentioned. While one might solicit these
contrasting word pairs from domain experts, our re-
sults show that statistical models such as SVM and
na¨ıve Bayes can automatically acquire them.
5.2 Identifying Perspectives at the Sentence
Level
In addition to identifying the perspective of a docu-
ment, we are interested in knowing which sentences
of the document strongly conveys perspective in-
formation. Sentence-level perspective annotations
do not exist in the bitterlemons corpus, which
makes estimating parameters for the proposed La-
tent Sentence Perspective Model (LSPM) difficult.
The posterior probability that a sentence strongly
covey a perspective (Example (6)) is of the most in-
terest, but we can not directly evaluate this model
without gold standard annotations. As an alterna-
tive, we evaluate how accurately LSPM predicts the
perspective of a document, again using 10-fold cross
validation. Although LSPM predicts the perspec-
tive of both documents and sentences, we will doubt
the quality of the sentence-level predictions if the
document-level predictions are incorrect.
The experimental results are shown in Table 5.
We include the results for the na¨ıve Bayes models
from Table 3 for easy comparison. The accuracy of
LSPM is comparable or even slightly better than that
of the na¨ıve Bayes models. This is very encouraging
and suggests that the proposed LSPM closely cap-
tures how perspectives are reflected at both the doc-
ument and sentence levels. Examples 1 and 2 from
the introduction were predicted by LSPM as likely to
114
Palestinian palestinian, israel, state, politics, peace, international, people, settle, occupation, sharon,
right, govern, two, secure, end, conflict, process, side, negotiate
Israeli israel, palestinian, state, settle, sharon, peace, arafat, arab, politics, two, process, secure,
conflict, lead, america, agree, right, gaza, govern
Table 4: The top twenty most frequent stems learned by the NB-M model, sorted by P(w|d)
Model Training Testing Accuracy
Baseline 0.5
NB-M Guests Editors 0.9327
NB-B Guests Editors 0.9346
LSPM Guests Editors 0.9493
NB-M Editors Guests 0.8485
NB-B Editors Guests 0.8585
LSPM Editors Guests 0.8699
Table 5: Results for Perspective Identification at the
Document and Sentence Levels
contain strong perspectives, i.e., large Pr(˜S = s1).
Examples 3 and 4 from the introduction were pre-
dicted by LSPM as likely to contain little or no per-
spective information, i.e., high Pr(˜S = s0).
The comparable performance between the na¨ıve
Bayes models and LSPM is in fact surprising. We
can train a na¨ıve Bayes model directly on the sen-
tences and attempt to classify a sentence as reflect-
ing either a Palestinian or Israeli perspective. A sen-
tence is correctly classified if the predicted perspec-
tive for the sentence is the same as the perspective
of the document from which it was extracted. Us-
ing this model, we obtain a classification accuracy of
only 0.7529, which is much lower than the accuracy
previously achieved at the document level. Identify-
ing perspectives at the sentence level is thus more
difficult than identifying perspectives at the docu-
ment level. The high accuracy at the document level
shows that LSPM is very effective in pooling evi-
dence from sentences that individually contain little
perspective information.
6 Conclusions
In this paper we study a new problem of learning to
identify the perspective from which a text is written
at the document and sentence levels. We show that
much of a document’s perspective is expressed in
word usage, and statistical learning algorithms such
as SVM and na¨ıve Bayes models can successfully
uncover the word patterns that reflect author per-
spective with high accuracy. In addition, we develop
a novel statistical model to estimate how strongly
a sentence conveys perspective, in the absence of
sentence-level annotations. By introducing latent
variables and sharing parameters, the Latent Sen-
tence Perspective Model is shown to capture well
how perspectives are reflected at the document and
sentence levels. The small but positive improvement
due to sentence-level modeling in LSPM is encour-
aging. In the future, we plan to investigate how con-
sistently LSPM sentence-level predictions are with
human annotations.
Acknowledgment
This material is based on work supported by
the Advanced Research and Development Activity
(ARDA) under contract number NBCHC040037.
A Gibbs Samplers
Based the model specification described in Sec-
tion 4.2 we derive the Gibbs samplers (Chen et al.,
2000) for the Latent Sentence Perspective Model as
follows,
pi(t+1) ∼ Beta(αpi +
Nsummationdisplay
n=1
dn + ˜d(t+1),
βpi +N −
Nsummationdisplay
n=1
dn + 1− ˜d(t+1))
τ(t+1) ∼ Beta(ατ +
Nsummationdisplay
n=1
Mnsummationdisplay
m=1
sm,n +
˜Msummationdisplay
m=1
˜sm,
βτ +
Nsummationdisplay
n=1
Mn −
Nsummationdisplay
n=1
Mnsummationdisplay
m=1
sm,n + ˜M −
˜Msummationdisplay
m=1
˜sm)
115
θ(t+1) ∼ Dirichlet(αθ +
Nsummationdisplay
n=1
Mnsummationdisplay
m=1
wm,n)
Pr(S(t+1)n,m = s1) ∝ P(Wm,n|Sm,n = 1,θ(t))
Pr(S(t+1)m,n = 1|τ,Dn)
Pr( ˜D(t+1) = d1) ∝
˜Mproductdisplay
m=1
dbinom(τ(t+1)d )
˜Mproductdisplay
m=1
dmultinom(θd,˜m(t))dbinom(pi(t))
where dbinom and dmultinom are the density func-
tions of binomial and multinomial distributions, re-
spectively. The superscript t indicates that a sample
is from the t-th iteration. We run three chains and
collect 5000 samples. The first half of burn-in sam-
ples are discarded.

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