(2014) "Acknowledging Discourse Function for Sentiment Analysis"

Acknowledging Discourse Function for
Sentiment Analysis
Phillip Smith and Mark Lee
University of Birmingham
School of Computer Science
Edgbaston, B15 2TT
[email protected], [email protected]
Abstract. In this paper, we observe the effects that discourse function
attribute to the task of training learned classifiers for sentiment analysis.
Experimental results from our study show that training on a corpus of
primarily persuasive documents can have a negative effect on the performance of supervised sentiment classification. In addition we demonstrate that through use of the Multinomial Na¨ıve Bayes classifier we can
minimise the detrimental effects of discourse function during sentiment
In discourse, sentiment is conveyed not only when a speaker is expressing a
viewpoint, but also when they are attempting to persuade. In this study we
examine the influence of these two functions of discourse on sentiment analysis.
We hypothesise that training a supervised classifier upon a document set of
a single discourse function will produce errors in classification if testing on a
document set of a different discourse function.
Ideally, we would have tested this theory on a currently used resource in
sentiment classification in order to examine and compare the behaviour of the
expressive and persuasive discourse functions in the overall classification process. However, no such resource is appropriately annotated with the expressive
and persuasive labels that are needed to test our hypothesis. We have therefore
developed a document set from the clinical domain, annotated on the document
level with discourse function information. The document set used for our experiments contains 3,000 short documents of patient feedback, which we have made
available online.1
We investigate our hypothesis by testing four supervised classifiers that are
commonly used in both the machine learning and sentiment analysis literature
[1]. The classifiers that we use are the simple Na¨ıve Bayes (NB), multinomial
Na¨ıve Bayes (MNB), logistic regression (LR) and linear support vector classifier (LSVC). We use both binary presence and term frequency features for each
classifier. We investigate our hypothesis by running four sets of experiments,
varying the training and testing sets of each. We run two across the same discourse function, expressive to expressive and persuasive to persuasive, and we
run two using the concept of transfer learning; expressive to persuasive and persuasive to expressive. Results of experiment exhibit decreases of up to 38.8%
F1 when training on the persuasive document set and testing on the expressive.
We also show that the classifier with the least variability in macro-average F1 is
the MNB classifier, which suggests its robustness to the the effects of discourse
function when performing supervised sentiment classification.
The remainder of this paper is structured as follows. Section 2 outlines the
theory of discourse function, and describes the nature of expressive and persuasive utterances that we encountered. In Section 3 we describe the corpus used
for experimentation. We describe how our experiments were set up in Section 4,
and in Section 5 we discuss our results and their relative implications. Finally,
we conclude and discuss avenues for future work in Section 6.
Discourse Function
Our study hinges on the premise that a difference in discourse function may be
detrimental to the use of supervised machine learning classifiers trained for sentiment analysis. We base our definition of discourse function on that proposed
by Kinneavy [2] who argues that the aim of discourse is to produce an effect
in the average reader or listener for whom the communication is intended. This
could be to share how one is feeling, or perhaps to persuade them. These two discourse functions fall into the expressive and persuasive categories, respectively.
Kinneavy also includes two other discourse functions, informative and literary,
in his theory of discourse [3].
To illustrate his theory, Kinneavy represents the components of the communication process as a triangle, with each vertex representing a different role in the
theory. This is somewhat similar to the schematic diagram of a general communication system that is proposed by Shannon [4]. The three vertices in the triangle
are labelled as the encoder, the decoder and the reality of communication. The
signal, the linguistic product, is the medium of the communication triangle. The
encoder is the writer or speaker of a communication, and the decoder is the
reader or listener.
In communication, when the language product is dominated by a clear design
of the encoder to discharge his or her emotions, or to achieve his or her own
individuality then it can be stated that the expressive discourse function is being
utilised [3]. In this paper, we take expression to be communicated through text.
Since the discourse function is in effect the personal state of the encoder, there
is naturally an expressive component in any discourse. We however narrow this
definition to only observe explicit examples of the expressive discourse function
in text.
Fig. 1. Kinneavy’s Communication Triangle [2]
We decompose the general notion of emotions that are conveyed to be valenced reactions, as either a positive or negative polarity based label. There is
little consensus as to the set of emotions that humans exhibit, however methods
have been put forward to extend these polarities into the realm of emotions [5,
6], so future work could extend this where needs be.
The components of expressive discourse when explicitly expressed are often
trivial to identify. Utterances beginning with the personal pronoun I followed by
an emotive verb often pertain to the expressive discourse function being utilised if
they are succeeded by an additional emotion bearing component. Much research
in sentiment analysis has observed the expressive discourse function [7–9].
Persuasion attempts to perform one or more of the following three actions: to
change a decoder’s belief or beliefs, to gain a change in a decoder’s attitude, and
to cause the decoder to perform a set of actions [10].
Sentiment can be viewed as a key component in persuasion, yet it is no trivial
feat to define what a positive persuasive utterance is. We define what we shall
call contextual and non-contextual persuasive utterances. First, let us observe
the non-contextual persuasive utterances. An example of a positive persuasive
utterance is: You should give him a pay rise. Taking this utterance alone, it is
clear that the encoder of the signal is attempting to persuade the decoder to give
someone more money for their work, which can be understood to be attempting
to elicit a positive action from the decoder, for the benefit of the subject of the
To contrast this, we must demonstrate a non-contextual negative persuasive
utterance. For example, take the utterance Please fire him. Here the encoder is
attempting to stop the subject of the utterance from working, by persuading
the decoder to ensure they cease working, which is typically seen as something
negative (at least in Western societies).
Davglength Wuniq.
750 47875
750 50676
750 44527
750 97408
Table 1. Persuasive & expressive corpus statistics.
Now, we must also consider the class of persuasive utterances that we describe
as ‘contextual’persuasive utterances. An example of such an utterance is: Please
give me a call. At first glance, this utterance lacks a clear sentiment. However if
we precede this with the sentence Great work!, the above persuasive utterance
becomes positive. However, if we precede out initial persuasive utterance with
the sentence You’ve messed up. our seemingly emotionless persuasive utterance
becomes negative. This agrees with the view of Hunston [11], that indicating an
attitude towards something is important in socially significant speech acts such
as persuasion and argumentation.
The corpus which we use in our experiments is the NHS Choices Discourse Function Corpus, introduced in [12]. This is a corpus of patient feedback from the
clinical domain. Patients were able to pass comments on hospitals, GPs, dentists and opticians through an online submission form. Whilst there were many
fields to fill in, the fields that were of relevance to sentiment analysis were those
labelled as ‘Likes’, ‘Dislikes’and ‘Advice’. These blanket labels help to define individual documents, and made the automatic extraction for experimentation a
straightforward process. There was also no need to hand-label the likes and dislikes for sentiment, as the labels presupposed this. Annotation was required for
the advice, as to whether a positive or negative sentiment was conveyed. This was
undertaken by two annotators, and inter-annotator agreement was measured.
Typically, sentiment analysis concentrates on the positive and negative aspects of a review; the likes and dislikes. However, the literature [3] has shown
that these expressive aspects of discourse function are not alone in communicating sentiment. As shown in earlier sections, the persuasive discourse function
also conveys sentiment when it is employed. Advice comes under the umbrella
term that is persuasion. When offering advice, the intention is often to persuade
the decoder of the advice to act in a certain manner, or to acquire a certain belief
set. This can be rephrased by saying that when we use the persuasive discourse
function, we often use advice to successfully perform this action. Therefore, in
this corpus, the comments of the likes and dislikes section of the corpus form the
expressive subsection, and the comments that the patients submitted under the
advice header form the persuasive subsection of the corpus.
In this paper we concentrate on a 3,000 document subset of the corpus. This
is divided into two 1,500 document sets for the documents that primarily used
the expressive and persuasive discourse functions respectively. The corpus can be
further divided into two 750 document subsets with documents communicating a
positive sentiment, and a negative sentiment. Table 1 outlines the token counts,
average document length, and the number of unique tokens present in each
section of the corpus that we used for experimentation. We should note that
there were at least 750 contributors to this corpus, and with the data being
mined from an online source, there were no stipulations as to the qualifications
of the poster, so the language model that would be learnt by the classifier would
have great linguistic variation.
In our experiments, we wanted to explore how supervised machine learning algorithms are able to generalise across discourse function. In particular we examine
the transferability of learned models trained on corpora of different discourse
function. Our hypothesis is that differences in discourse function will detract
from the transferability of learned models when detecting sentiment, if they
are tested on datasets of differing discourse function. We experiment across all
pairwise combinations of the training and testing document set. We initiate the
experiments in this way so that the directionality of discourse function could
be tested, along with the transferability of the learned models across discourse
function. We use scikit-learn [13] and NLTK [14] Python packages for our classifiers.
When training our models (NB, MNB, LR and LSVC), we used the same
data for each algorithm. The training set consisted of 1,000 training documents,
500 positive and 500 negative, for both of the respective discourse functions.
The test set consisted of 500 documents, 250 positive and 250 negative, from
each discourse function. These were randomly selected from the NHS Choices
Discourse Function corpus [12], however ensuring that there is no overlap between the sets. For the expressive to expressive and persuasive to persuasive
experiments, 10-fold cross validation of the machine learning methods was used.
Results & Discussion
Figure 2 shows the macro-average F1 values for each experimental setup. Classifiers that are trained upon the expressive document set perform better than
those trained on the persuasive document set, irrespective of classifier choice or
feature set used. The NB classifier shows greatest variability in classifier performance, with a peak F1 of 0.826 and a minima of 0.438. The LR and LSVC
models also exhibit a degree of variation in F1 . The MNB classifier minimises
the variability in performance, and is the most robust classifier that we tested.
Fig. 2. Macro-averaged F1 results for the cross-validation and transfer learning experiments.
Where other classifiers struggle when training on the persuasive document set,
MNB achieves a macro-average F1 of 0.802.
The results show the relative ease with which the expressive document set
is able to create learned models of sentiment, and apply both to test sets of
either an expressive or persuasive discourse function. When comparing crossvalidation results to those of the transfer learning experiments, results exhibit
minimal disturbance in macro-average F1 score when models are trained on a
corpus of expressive documents. This does not therefore support our hypothesis
in the instance where we use the expressive document set to train our classifiers.
However, this is only for the expressive function.
These results suggest that if there were a hierarchy of discourse functions,
then persuasion is perhaps a subset of expression, and it inherits elements of the
expressive vocabulary in order to carry out its role. We base this on the results of
classification from the expressive to persuasive, and the poor adaptation of any
classifiers trained on the persuasive document set. Consequently, we are inclined
to believe that the persuasive discourse function cannot fully function without
expressive elements. Examples of this are appeals to emotional elements, such
as in congressional debates [15], where persuasion through fact alone are not the
sole tactics used to sway the voters.
There is a clear drop in classifier performance when training on the persuasive corpus, and performing transfer learning. This supports our hypothesis for
all classifiers, where each classifier trained in this way underperforms, sometimes
to a considerable degree. We believe that this could be due to the implicit nature of sentiment that the persuasive discourse function conveys, and could be
attributed to the structure of a text, in particular the interface between syntax
and lexical semantics [16]. Further work is required to examine the differences
in structure between documents of the respective discourse functions in order to
confirm this assumption.
One interesting classifier is the MNB classifier. This performed consistently
well during our study, and was even able to cope with the effects of crossdiscourse classification to a high degree, performing well on the difficult persuasive to expressive classification experiments. We believe that this is due to
the minimization in error rate that it has previously been shown to achieve, as it
is able to deal with overlapping vocabularies and variable document length [17].
This performs considerably better than the simple NB classifier, and we believe
that this is due to the difference in feature distribution that is observed in the
This paper has observed the effects of discourse function on supervised machine
learning approaches to sentiment analysis. The effects of classification across
the expressive and persuasive discourse function were recorded, and we found
that despite both discourse functions conveying sentiment, the corpus with documents primarily utilising the expressive discourse function was preferable to train
learned models upon, in comparison to a document set of primarily persuasive
documents. In empirical results on a corpus of patient feedback containing documents of both discourse function testing across discourse, we found that there
was an average improvement in accuracy of up to 38.8% when using the expressive subcorpus instead of the persuasive as a training set. We also find that the
MNB classifier is preferable to others in order to minimise the effects of discourse
function on sentiment classification.
In future work we will investigate further the effects of discourse function on
other learned classifiers in order to determine if any others are able to minimize
its effects on supervised machine learning models.
1. Liu, B.: Sentiment Analysis and Subjectivity. Handbook of Natural Language
Processing 2 (2010) 568
2. Kinneavy, J.E.: The Basic Aims of Discourse. College Composition and Communication 20 (1969) 297–304
3. Kinneavy, J.L.: A Theory of Discourse: The Aims of Discourse. Norton (1971)
4. Shannon, C.E.: A Mathematical Theory of Communication. Bell Systems Technical
Journal 27 (1948) 379–423
5. Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)
6. Smith, P., Lee, M.: A CCG-based Approach to Fine-Grained Sentiment Analysis. In: Proceedings of the 2nd Workshop on Sentiment Analysis where AI meets
Psychology, The COLING 2012 Organizing Committee (2012) 3–16
7. Mullen, T., Collier, N.: Sentiment Analysis using Support Vector Machines with
Diverse Information Sources. In Lin, D., Wu, D., eds.: Proceedings of EMNLP
2004, Association for Computational Linguistics (2004) 412–418
8. Bloom, K., Garg, N., Argamon, S.: Extracting Appraisal Expressions. In: Human
Language Technologies 2007: The Conference of the North American Chapter of the
Association for Computational Linguistics; Proceedings of the Main Conference,
Association for Computational Linguistics (2007) 308–315
9. Dermouche, M., Khouas, L., Velcin, J., Loudcher, S.: AMI&ERIC: How to Learn
with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis. In:
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation
(SemEval 2013), Association for Computational Linguistics (2013) 364–368
10. Miller, G.R.: 1. In: The Persuasion Handbook: Developments in Theory and
Practice. Sage (2002) 3–17
11. Hunston, S.: Corpus Approaches to Evaluation. Routledge (2011)
12. Smith, P., Lee, M.: Cross-discourse Development of Supervised Sentiment Analysis
in the Clinical Domain. In: Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, Association for Computational
Linguistics (2012) 79–83
13. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.,
Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A.,
Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine
learning in Python. Journal of Machine Learning Research 12 (2011) 2825–2830
14. Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. OReilly
Media Inc (2009)
15. Guerini, M., Strapparava, C., Stock, O.: Resources for Persuasion. In: Proceedings
of LREC 2008, European Language Resources Association (2008) 235–242
16. Greene, S., Resnik, P.: More than Words: Syntactic Packaging and Implicit Sentiment. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics (2009) 503–511
17. McCallum, A., Nigam, K., et al.: A Comparison of Event Models for Naive Bayes
Text Classification. In: AAAI-98 Workshop on Learning for Text Categorization.
Volume 752., AAAI (1998) 41–48