=Paper=
{{Paper
|id=Vol-2918/paper7
|storemode=property
|title=Explaining Sentiment from Lexicon
|pdfUrl=https://ceur-ws.org/Vol-2918/paper7.pdf
|volume=Vol-2918
|authors=Sergio Consoli,Luca Barbaglia,Sebastiano Manzan
|dblpUrl=https://dblp.org/rec/conf/esws/ConsoliBM21
}}
==Explaining Sentiment from Lexicon==
https://ceur-ws.org/Vol-2918/paper7.pdf
Explaining Sentiment from Lexicon
Sergio Consolia , Luca Barbagliaa and Sebastiano Manzana
a
European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra (VA), Italy
Abstract
Lexicon-based Sentiment Analysis relies on sentiment dictionaries which are used to assign a sentiment
polarity to the words of an input text. The overall sentiment of the text is then computed by means of
a combining function, such as the word count, sum or average. In this short contribution we describe
a detailed set of linguistic rules that allow to understand the text fragments which are semantically
linked to a given concept of interest in a text. These heuristics have been designed in the spirit of the
recent Interpretable AI trend, since they allow to understand the origin of sentiment for a specific term,
providing more transparency and interpretation of the resulting analysis, and enabling the development
of advanced and novel lexicon-based Sentiment Analysis approaches, which is the object of our currently
on-going work.
Keywords
Lexicon-based Sentiment Analysis, Natural Language Processing, Interpretability, Rule-based models
1. Introduction
The rapid advances in information and communications technology experienced in the last two
decades have produced an explosive growth in the amount of information collected, leading
to the new era of Big Data [1]. This has brought to the exponential increase in the informa-
tion available in various domains, allowing for Natural Language Processing (NLP) and novel
knowledge generation methods to emerge in different sectors. In particular, utilizing the senti-
ment extracted from social media has long been the tradition of several studies [2]. As the Web
rapidly grows and evolves, people are becoming increasingly enthusiastic about interacting,
sharing, and collaborating through social networks, online communities, blogs, and wikis [3].
Therefore, it is critical to correctly interpret sentiments and opinions expressed or reported
about social events, political movements, company strategies, marketing campaigns, and any
other form or online interaction.
Sentiment Analysis (SA) [4, 5], also known as Opinion Mining, is a Semantic Web technology,
directly related to Natural Language Processing, that aims at understanding whether a certain
textual message conveys a positive or negative sentiment with respect to a certain topic, or the
overall contextual polarity or emotional reaction to a document, interaction, or event [4, 5]. Its
outcome might be a quantitative/qualitative polarity (e.g., [−1 ∶ 1], extr neg, neg, neut, pos, extr
pos, etc.) or an emotional state (e.g., joy, anger, etc.).
X-SENTIMENT: 6th International Workshop on eXplainable SENTIment Mining and EmotioN deTection, co-located at
ESWC, June 07, 2021, Hersonissos, Greece
" sergio.consoli@ec.europa.eu (S. Consoli)
0000-0001-7357-5858 (S. Consoli); 0000-0001-5930-5392 (L. Barbaglia)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
SA can be performed using both machine learning methods (see e.g., [6, 7, 8]) and lexicon-
based methods (see e.g., [9, 10, 11, 12, 13]). Models driven by machine learning algorithms and
vector representations have achieved top performance for various SA tasks. Although these
models may get very accurate results, they provide however a limited understanding of patterns
and features used to correctly classify the input text into sentiment categories. Therefore, these
models lack transparency, traceability, and explainability on how the decisions are taken. In
addition, another main disadvantage of machine learning models for sentiment analisis consists
in their dependence on labelled data used for model training: it is not always easy to ensure
that sufficient and correctly labelled data can be obtained for specific domains.
Conversely, lexicon-based approaches to SA are completely unsupervised and do not require
any a-priori training corpus. They rely instead on dictionaries of words with assigned positive
or negative sentiment polarity scores, also referred to as sentiment dictionaries or lexicons,
like, for instance, SentiWordNet [14]1 , SenticNet [15, 16]2 , and Harvard IV-43 , just to name few
popular ones. Most of these sentiment dictionaries are freely available online and have been
often reused by the interested scientific and professional communities in several applications.
Given a sentence, a lexicon-based SA approach works by assigning positive and negative sen-
timent polarity values from the dictionary to all the words in the sentence, and a combining
function, such as the word count, sum or average [4], is used to aggregate the scores into the
overall sentiment of the text. In this way, developers are relieved from collecting and labeling
a large, relevant training corpus, at the minor cost of re-using an already existing sentiment
dictionary, or constructing, if needed, a customized sentiment lexicon for a specific application.
In addition, a lexicon-based SA method can be more easily understood and eventually modi-
fied by a human in comparison to a machine learning approach to SA, providing a significant
advantage towards interpretability of the model results.
Most lexicon-based SA methods focus on a coarse-grained analysis of the sentiment ex-
pressed in the text [4], that is, they assess the entire sentiment of a sentence by considering all
expressions of positive and negative sentiment contained in that text. However, coarse-grained
methods might not be precise enough in evaluating the sentiment polarity of a specific con-
cept of interest contained in a sentence, given that the sentiment of the entire text is often not
expressed towards that specific topic [17].
In currently on-going research, we are investigating a fine-grained perspective to lexicon-
based SA. In particular, we are interested in understanding the parts of the text which convey a
sentiment connotation with respect to a specific concept of interest, and properly propagating
these sentiments towards an overall computed sentiment score for the topic. While the entire
approach is currently under development, we report here the set of linguistic polarity rules used
to identify the text fragments semantically connected to a specific concept of interest within
a sentence, possibly expressing a sentiment connotation towards it. We believe that explicit
semantics can be leveraged to explain why a resource has been scored in a specific sentiment
category, inducing trustworthiness and avoiding biases, and accompanying the current model
interpretability trend in AI aiming at opening up the black-box by providing a narrative of the
1
SentiWordNet, version 3.0, available at: https://github.com/aesuli/SentiWordNet.
2
SenticNet, available at: https://sentic.net/.
3
The details of the latest version of the Harvard IV-4 dictionary are available at: http://www.wjh.harvard.edu/
~inquirer/homecat.htm.
Table 1
Used spaCy part-of-speech tags.
TAG POS DESCRIPTION
CC CONJ conjunction, coordinating
IN ADP conjunction, subordinating or preposition
JJ ADJ adjective
JJR ADJ adjective, comparative
JJS ADJ adjective, superlative
MD VERB verb, modal auxiliary
NN NOUN noun, singular or mass
NNP PROPN noun, proper singular
NNPS PROPN noun, proper plural
NNS NOUN noun, plural
RBR ADV adverb, comparative
RBS ADV adverb, superlative
VB VERB verb
Table 2
Used spaCy dependency parsing classes.
DEP DESCRIPTION
acl clausal modifier of noun (adjectival clause)
advcl adverbial clause modifier
advmod adverbial modifier
amod adjectival modifier
attr attribute
dobj direct object
neg negation modifier
oprd object predicate
pcomp complement of preposition
pobj object of preposition
prep prepositional modifier
xcomp open clausal complement
underlying model [18, 19]. Our methodology under development goes into this direction.
2. Set of linguistic rules for lexicon-based Sentiment Analysis
We provide here details on the semantic rules used to detect the text fragments semantically
connected to a specific concept of interest in an input text. These linguistic rules have been
derived experimentally after in-depth natural language analysis [20], and are based on both
syntax and semantics of the text. Each rule can be seen as a single building block, and the
concatenation of these rules enable to explain how a particular sentiment polarity score is
evaluated by an underlying lexicon-based SA algorithm, providing more transparency and in-
Figure 1: An illustration on the focused fragment of text for the sentence: Despite Ronaldo’s age, his
physical shape looks perfect so far.
terpretation of the resulting analysis.
The overall process is based on the linguistic features of the spaCy 4 Python library. Tables
1-2 present the main labels assigned by spaCy with respect to part-of-speech tagging and de-
pendency parsing, respectively, that we use in our rule scheme. Based on the POS (i.e., the
detected part-of-speech), DEP (i.e., the parsed dependency), and TAG (i.e., the tag of the part-
of-speech) labels defined in these tables, the algorithm selects a chunk in a sentence only if it
contains a certain concept of interest (Concept) specified as input, and falls into one of the
adopted semantic rules, detailed in the following.
Concept connected to a verb followed by an adjectival complement.
The Concept of interest is associated to a verb (POS = VERB) which is followed by an ad-
jectival complement relation (DEP = acomp), which means that it connects the verb to an
adjectival term which functions as the complement (like an object of the verb) and offers more
information about it. The adjective (POS = ADJ) can be in the form of:
a. standard adjective (TAG = JJ);
Example (Figure 1): ...despite Ronaldo’s age, his physical shape looks perfect so far...
acomp
(Concept=shape→VERB=looks−−−−−→ JJ=perfect).
Concept connected to a verb associated to a noun.
In this case Concept is connected to a verb (POS = VERB) which is linked to a noun (POS =
NOUN) by means of one of the following relations:
a. direct object (DEP = dobj), i.e. a clause which connects a transitive verb to a nominal
representing the recipient of the action of such predicate;
Example: ...last year Michael received an award for his work...
dobj
(Concept=Michael→ VERB=received −−−→NOUN=award).
4
spaCy: Industrial-Strength Natural Language Processing in Python. Available at: https://spacy.io/.
b. attribute (DEP = attr), i.e. a clause which connects a copula verb to a noun being the
non-verb phrase predicate of such verb;
Example: ...in Catalonia taxation has been an heavy deterrent on the development of SMEs...
attr
(Concept=taxation→ VERB=been−−→NOUN=deterrent).
Concept connected to a verb followed by an adverbial modifier.
The Concept of interest is associated to a verb (POS = VERB) which is followed by an adverbial
modifier relation (DEP = advmod), which means that it connects the verb to a non-clausal
adverb or adverbial phrase that serves to modify the predicate. The adverb (POS = ADV) can
be in the form of:
a. comparative adverb (TAG = RBR);
Example: ...his power will decline further...
advmod
(Concept=power→VERB=decline−−−−−−→ RBR=further).
b. superlative adverb (TAG = RBS);
Example: ...Tim’s attention is best focused on one thing: football!
advmod
(Concept=attention→VERB=focused −−−−−−→ RBS=best).
Concept connected to a verb followed by an object predicate.
The Concept of interest is associated to a verb (POS = VERB) which is followed by an object
predicate relation (DEP = oprd), that is a non-verb phrase predicate in a small clause that
functions like the predicate of an object. It means, in other words, that the linked verb is
connected to an adjective that qualifies, describes, or renames the object that appears before
it. The adjective (POS = ADJ) can be in the form of:
a. standard adjective (TAG = JJ);
Example: ...the law has been declared unconstitutional...
oprd
(Concept=law→VERB=declared −−−→ JJ=unconstitutional).
b. comparative adjective (TAG = JJR);
Example: ...the ECB kept the rates lower than expected...
oprd
(Concept=ECB→VERB=kept −−−→ JJR=lower).
c. superlative adjective (TAG = JJS);
Example: ...the FED is keeping asset prices the lowest...
oprd
(Concept=FED→VERB=keeping −−−→ JJS=lowest).
Concept connected to a verb followed by a prepositional modifier.
The Concept of interest is connected to a verb (POS = VERB) which is followed by a preposi-
tional modifier relation (DEP = prep), that is a prepositional phrase that modifies the heading
verb. The propositional modifier is linked to an adposition (POS = ADP), which basically es-
tablishes a grammatical relationship that links its complement to another word or phrase in the
context. An adposition typically establishes a semantic relationship which may be spatial (in,
on, under, ...), temporal (after, during, ...), or of some other type (of, for, via, ...). The adposition
is then connected to one of the following terms:
a. a noun (POS = NOUN), by means of an object of preposition relation (DEP = pobj), i.e.
a noun phrase that follows a preposition and completes its meaning;
Example: ...currently Ronaldo is in great shape...
prep pobj
(Concept=Ronaldo→ VERB=is−−−→ADP=in−−−→NOUN=shape).
b. a verb (POS = VERB), by means of a complement of preposition relation (DEP = pcomp),
i.e. a clause which is not a pobj and directly connects the preposition with any dependent
completing its meaning;
Example: ...The firm is lowering its profits after paying 1 million euros to the tax office...
prep pcomp
(Concept=firm→VERB=lowering −−−→ADP=after −−−−−→NOUN=paying).
Concept connected to a verb followed by an open clausal complement or an
adverbial clause modifier.
The Concept of interest is in this case associated to a verb (POS = VERB) which is connected
to another term by means of one of the following relations:
a. open clausal complement (DEP = xcomp), i.e. a predicative or clausal complement with-
out its own subject;
Example: ...news reported that the the FTSE index could keep loosing...
xcomp
(Concept=FTSE index→VERB=keep−−−−−→VERB=loosing).
b. adverbial clause modifier (DEP = advcl), i.e. a clause which modifies a verb or another
predicate (adjective, etc.) as a modifier, not as a core complement;
Example: ...industrial production will reach the bottom as it struggles with the current crisis
given by the pandemic...
advcl
(Concept=industrial production→VERB=reach−−−−→ VERB=struggle).
Concept associated to an adjectival clause.
The Concept of interest is connected by an adjectival clause (DEP = acl), i.e. a finite or non-
finite clause that modifies Concept, to a term being:
a. a verb (POS = VERB);
Example: ...there exist many tools providing similar benefits...
acl
(Concept=tools−−→VERB=providing).
Concept associated to an adjectival modifier.
In this case Concept is connected by an adjectival modifier relation (i.e., DEP = amod), i.e. an
adjective phrase that modifies the meaning of the Concept of interest, to a term being:
a. an adjective (POS = ADJ), in the form of:
i. standard adjective (TAG = JJ);
Example: ...his top performance is encouraging the rest of the team...
amod
(Concept=performance−−−−→JJ=top).
ii. comparative adjective (TAG = JJR);
Example: ...there is a larger consumption than in the past years...
amod
(Concept=consumption−−−−→JJR=larger).
iii. superlative adjective (TAG = JJS);
Example: ...the manufacturing sector is experiencing the worst decline since World
War II...
amod
(Concept=decline−−−−→JJS=worst).
b. a verb (POS = VERB);
Example: ...overall it appears to be an encouraging agreement...
amod
(Concept=agreement −−−−→VERB=encouraging).
3. Conclusion and Outlook
This is on-going research. We are aware that the presented linguistic rules are heuristics de-
rived experimentally after in-depth natural language analysis. They further require a rigorous
testing, which we aim in our currently on-going work.
In the near future we are planning to implement an advanced lexicon-based SA method
leveraging on the described linguistic rules, aiming also at an in-depth performance compari-
son against other popular SA approaches. This strategy under current development will focus
in particular on the economic and financial domains [21], with the goal of providing useful
signals to improve forecasting and nowcasting of economic and financial indicators.
4. Acknowledgments
The authors would like to thank the colleagues of the Centre for Advanced Studies at the
Joint Research Centre of the European Commission for helpful guidance and support during
the development of this research work. The views expressed are purely those of the authors
and may not in any circumstance be regarded as stating an official position of the European
Commission. The author Dr. Sergio Consoli is also particularly grateful to the Wagner team in
Catania, Italy, for inspiring discussions, precious advices, and support.
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