=Paper= {{Paper |id=Vol-2293/paos2018-passcr2018_paper6 |storemode=property |title= Knowledge-based Identication of Emotional Status on Social Networks |pdfUrl=https://ceur-ws.org/Vol-2293/paos2018-passcr2018_paper6.pdf |volume=Vol-2293 |authors=Julio Vizcarra,Kouji Kozaki,Miguel Torres,Rolando Quintero |dblpUrl=https://dblp.org/rec/conf/jist/VizcarraKRQ18 }} == Knowledge-based Identication of Emotional Status on Social Networks == https://ceur-ws.org/Vol-2293/paos2018-passcr2018_paper6.pdf

Knowledge-based Identication of Emotional
Status on Social Networks
1
Julio Vizcarra , Kouji Kozaki
1,∗ 2
,Miguel Torres Ruiz ,Rolando Quintero
2

1
The Institute of Scientic and Industrial Research (ISIR) Osaka University
Mihogaoka 8-1, Ibaraki, Osaka 567-0047. Japan.
2
Centro de Investigación en Computación CIC , Instituto Politécnico Nacional,
UPALM-Zacatenco, CIC. Building, 07738, Mexico City, Mexico.

Abstract. A knowledge based methodology is proposed for the content
understanding and sentiment identication of the shared comments in
social networks. The goal of this work is to retrieve the sentiment in-
formation associated to an opinion and classify it by its polarity and
sentiment by means of a semantic analysis. Our approach implements
knowledge graphs, similarity measures, graph theory algorithms and dis-
ambiguation processes. The results obtained were compared with data
retrieved from Twitter and users' reviews in Amazon. We measured the
eciency of our contribution with precision, recall and F-measure com-
paring it with the traditional method of just looking up concepts in sen-
timent dictionaries which usually assigns averages. Moreover an analysis
was carried out in order to nd the best performance for the classication
by using polarity, sentiment and a polarity-sentiment hybrid . A study is
presented for remarking the advantage of using a disambiguation process
in knowledge processing.

Keywords: sentiment analysis, knowledge engineering, conceptual similarity

1 Introduction
Nowadays the huge information transmitted on social networks has become a
rich source of information for the human understanding as well as a way of
expression where the users share their sentiment status and personal opinions
through comments. The sentiment identication can classify comments as posi-
tive or negative(polarity) and unveil emotions such as anger, trust, sadness ,etc.,
on certain topics or users. Moreover the sentiments presented in the opinions
can be relevant in the design of custom services, social plans for public health,
marketing, e-commerce,etc.
On the hand sentiment analysis has become one of the fastest growing re-
search areas in computer science due the outbreak of computer-based sentiment

* Corresponding author.
E-mail address: kozaki@ei.sanken.osaka-u.ac.jp.
2 Julio Vizcarra et al.

studies with the availability of subjective texts on the Web [16]. Furthermore
the sentiment analysis has gained attention over the years in the general public
as it is currently shown in Google trends [10].
Based on the previous motivation the present work aims in the identication
of sentiment information in opinions on social networks. Our approach explores
a content-based and semantic processing of the knowledge implicit in the com-
ments. For each opinion we created a formal representation which it is associated
with a sentiment and polarity.

2 Background
This section lists some relevant works related with the proposed methodology
presenting their key features. As summary we present a discussion where we
remark the main contributions of our work.
Describing briey some similar works related with sentiment analysis are:
Anja Rudat[20] explored the criteria inuencing selection for retweeting in Twit-
ter. Trying to discover relations on social networks Yuan Wang[24] proposed a
methodology that inferred social relationships in microblogs based on physical
interactions using user's location records. The work of Garcia-Pablos [7] pro-
posed an unsupervised system for the aspect-based sentiment analysis. One of
the limitation of this work was the necessary to dene manually seed concepts
and domains as input of the methodology. The work of Divya Sehgal et al., [21]
proposed a real-time sentiment analysis using dictionaries but mostly focused
on big data techniques that prioritize the velocity instead of a deeper analy-
sis. Theodore Georgiou [8] proposed a community detection algorithm utilizing
social characteristics and geographic locations.
Regarding the semantic processing the work of Shivam Srivastava [22] devel-
oped an algorithm to cluster places not only based on their locations but also
their semantics in social networks, the contributions of this work was the geo-
social clustering from check-in data. The work of Shuai Wang et al.[23] applied
a semantics-based learning technique for a set of concepts previously labeled
by grouping the target-related words in order to extract the semantics among
words.
On the other side some researches related to social networks analysis are for
instance the work of Shuiguang Deng[5] that proposed a recommendation service
for the social networks with a trust enhancement method. Considering the in-
uence on social networks the work of Meng Jiang[14] studied the interpersonal
inuence, the approach explains the importance of this factor for behavior pre-
diction. Additionally Huang Liwei[12] explored the user preference, social and
geographical inuence in order to recommend proper POIs (Point-of-interest).
The machine learning implementation of Souvick Ghosh[9] processed the media
text in order to determine the polarity and sentiment using manually labeled
Facebook posts.
Reviewing the state-of-the-art, most of the researches worked with key social
attributes that in general dismissed the semantics focusing in the lexical process-
Knowledge-based Identication of Emotional Status on Social Networks 3

ing, keywords or explicit reactions in the social media. About the methodologies
that implemented machine learning techniques they were based on a high quality
large training datasets on a specic domain. On the hand our work handles the
comments as excerpt of the knowledge, in this gap we prioritized the semantic
level, sense and meaning of the whole comment. The proposal computed semantic
similarity measures, conceptual expansion, graph theory algorithms and disam-
biguation using on a multi domain knowledge base. The methodology is exible
which implies that the domains can be adjusted by just modifying knowledge
base.

3 Methodology
This section describes the methodology in three main stages. The rst stage
social networks discovery retrieves opinions from events or public proles by
reading comments in photos, posts, videos, etc. The stage of knowledge process-
ing constructs the formal representation for each comment. This module carries
out processes of automatic knowledge graph construction enhanced by disam-
biguation. Finally the stage of sentiment analysis estimates the total polarity
and main sentiment in the comments .

3.1 Social network discovery stage
In the stage the comments are retrieved from public events or user proles on
social network. This process obtains users, comments and the social graph's
structure.

3.2 Knowledge processing stage
In this stage a content-based formal representation is constructed for each com-
ment in the social network. This stage is composed by lexical preprocessing
, knowledge graph expansion , similarity measure and disambiguation .

Lexical pre-processing. In the step the concepts in a comment are processed
in order provide term matching with the knowledge base. The processes consid-
ered are: stop words elimination, tokenizer, stemming, and removal of unknown
concepts in the knowledge graph.

Knowledge graph expansion. In this step the set of concepts obtained in the
lexical processing are expanded on the knowledge graph until nding a common
root for all their senses.
Let us dene G(C, R) as a knowledge graph with the set of concepts C and
the set of relationships R; the knowledge base expansion (Ge)(equations 1, 2) for
a concept c ∈ C is the iterative process (α iteration) of discovering new concepts
4 Julio Vizcarra et al.

in knowledge graph (G) using semantic relations (ρ)(equation 4) that connect a
origin concept c to the other destination concepts Cα(equation 3).

Geρ0 (c, G) = G0 (C0 , R0 ) = G0 ({c}, ∅) (1)

Geρα (c, G(C, R)) = Gα (Cαρ , Rα
ρ
) (2)

(
α = 0 {c}
Cαρ = (3)
α > 0 Cα−1 ∪ {y ∈ C : x ∈ Cα−1 , ρ(x, y) ∈ R}

(
ρ α=0 ∅
Rα = (4)
α > 0 {ρ(x, y) ∈ R : x, y ∈ C, x ∈ Cα−1 }

Similarity Measurement. Once the concepts were expanded and an excerpt of
knowledge was constructed from the previous stage, the next step is to establish
similarity measures among all concepts. In order to accomplish this task two
dierent approaches were implemented:

1) Automatically. It was implemented the similarity measure of conceptual
distance DIS-C[19] that automatically establishes the similarity among concepts
following the idea of visibility in the knowledge graph.

2) Manually. For each semantic relationship in the knowledge graph we es-
tablished a weight in the range [0,1].

Disambiguation. In this stage a strongly connected graph GD (C, R) is cre-
ated which is disambiguated and reduced (number of nodes and relationships)
by a steiner tree algorithm. In the methodology we implemented the SketchLs
algorithm[11] due the capability of handling large graphs. The disambiguation
process starts counting the number of occurrences(senses)(Figure 1). If a con-
cept has only one occurrence it implies that it has only one sense and it will
participate in the disambiguation of the other concepts. On the other hand if a
concept has more than one occurrence this concept has to be disambiguated.

During the disambiguation if the comment has only one concept and it has
several senses then a dictionary of polysemy has to be consulted for nding most
probable sense. On the other hand if the comment has more than a concept then
the disambiguation will be computed.
Knowledge-based Identication of Emotional Status on Social Networks 5

Fig. 1. Disambiguation

3.3 Sentiment analysis stage
Polarity calculation. In this step the polarity for comment is calculated
P olarity(Comentx ) taking into account the individual polarity of each concept
P o (CP ). The process starts dividing the concepts in subsets Cx considering their
positive or negative polarity Po(Cx )(see equations 5-6). In order to calculate the
polarity Pot(Xg ) for a set of concepts Xg the arithmetic mean is computed (equa-
tion 7). The total polarity of a comment P olarity(Comentx ) is calculated by the
sum of positive plus negative polarities XP and XN respectively(see equation 8).

XP = {Cx | P o (Cx ) > 0; Cx XP } (5)

XN = {Cx | P o (Cx ) < 0; Cx XN } (6)
Pn
P o (Cx )
P ot (Xg ) = i=0 ; Cx Xg (7)
n
P olarity(Commentx ) = P ot (XP ) + P ot (XN ); XN , XN ⊆ Commentx (8)

Sentiment identication. In this step the sentiment status is identied in
a comment Sentiment(Comentx ) . For each concept Ci ∈ Comentx , Ci it is
expanded in the knowledge graph until nding one or more concepts linked to a
sentiment Sx . The next process is to nd the the closest sentiment Sx to Ci by
computing a shortest path algorithm and semantic similarities. Consecutively a
pre-dened numerical weight W s(Cx ) is assigned for the sentimentSx which is
located between the range [-1,-1] (equation 9). Once the weight of the sentiment
was obtained the next step is to calculate the sentiment value Sen(C)x) for the
concept Cx by multiplying the sentiment weight W s(Cx ) by its polarity P o(Cx )
(equation 10). Finally the sentiment status with the highest sentiment value
Sen (Cx ) is assigned to the comment Comentx (equation 11).

W s (Cx ) = w (Sx ) ; Cx → Sx , w (Sx ) ∈ [−1, 1] (9)

Sen (Cx ) = P o (Cx ) W s (Cx ) (10)

Sentiment(Commentx ) = max ({Sen (Ci ) | Ci ∈ Comentx }) (11)

The gure 2 presents the iterative process of expansion for nding the sen-
timent associated to a concept Cx in the knowledge base. When one or more
6 Julio Vizcarra et al.

concepts are located and they are linked to a sentiment then the Dijkstra algo-
rithm with Fibonacci heap [6] is executed in order to select only one concept.

Fig. 2. Sentiment identication

4 Implementation
This section presents the results after implementing the described methodology.
It is divided in two subsections: knowledge bases and sentiment analysis.

4.1 knowledge bases
In this section we describe the knowledge base's structure which is composed
by: general knowledge graphs for common language understanding on several
domains and sentiment dictionaries mapped into the knowledge graph.

General knowledge bases
WordNet[1] (version 3.1) is a large lexical database of English. Nouns, verbs,
adjectives and adverbs are grouped into sets of cognitive synonyms (synsets).
The Japanese WordNet[3,13] is similar to Wordnet for processing the Japanese
language.
Open Multilingual Wordnet [4][3] provides access to wordNets in a variety
of 34 languages merged into English WordNet.

Sentiment dictionaries
SentiWordnet [2] is a lexical resource that assigns polarity values to concepts
in English WordNet.
NRC_emotion_lexicon [18,17] is a list of English words associated with
eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy,
and disgust).
Knowledge-based Identication of Emotional Status on Social Networks 7

4.2 Sentiment Analysis
In order to explain the results obtained in the sentiment analysis an example was
processed from Twitter in the CNN News account. The comment considered is :
a number of people feared dead after a dam bursts in kenya with hundreds left
homeless ocials say . The table 1 presents the closest sentiment and a polarity
value assigned by our methodology to each concept.

Id Wordnet-Concept Sentiment with polarity

WN:107449542-n ("are",burst) Sentiment:NRC_fear_NRC_anger|:Polarity:-0.25 ,
WN:107964900-n (homeless) Sentiment:NRC_anticipation_disgust_anger|Polarity:-0.125 ,
WN:107534492-n (fear) Sentiment:NRC_fear,sadness,anger,surprise|Polarity:-0.875 ,
WN:114509110-n (say) NRC_surprise_anticipation|Polarity:0.5
Table 1. Sentiment-Polarity assigned to concepts

Finally the methodology estimates the total polarity and main sentiment
presented in the comment(table 2).

Sentiment Polarity Comment

NRC_Anger -0.1875 a number of people feared dead after a dam bursts in kenya with
hundreds left homeless ocials say.
Table 2. Sentiment-Polarity assigned to comment

Other relevant examples from the CNN news account are presented in table 3.
We noticed a better classication using the basic sentiments instead of polarity.

Sentiment Polarity Comment

trust 0.2916667 This couple found a buried safe containing $52,000 worth of money,
gold and jewelry in their backyard, but didn't keep it
trust -0.15 In an eort to keep conversations and search results on topic, Twitter
announced it will use new "behavioral signals" to push down more
tweets that "distort and detract"
anger 0.04166667 A massive poaching ring in Oregon and Washington is accused of
killing more than 200 animals including deer, bears, cougars, bobcats
and a squirrel
anger 0.041666687 An estimated 239,000 girls under the age of ve die in India each year
due to neglect linked to gender discrimination, a new study nds
sadness 0.25 @CNN Her father had a heart surgery and cant walk so
sadness -0.25 Teen develops 'wet lung' after vaping for just 3 weeks
joy 0.125 I am proud to be a woman and a feminist. The politics of Meghan
Markle
Table 3. Other examples processed in twitter
8 Julio Vizcarra et al.

5 Evaluation
This section measured the performance of our methodology comparing it with
labeled data with sentimental information. We considered as a manual processing
Twitter posts that we manually labeled and as automatic processing comments
ranked by the users in amazon reviews. As traditional method (baseline) we
proposed the process of only looking up concepts with polarity in dictionaries.

5.1 Sentiments evaluation on Amazon Reviews
We evaluated our work with precision, recall and F-measure over 10 000 com-
ments using the dataset Amazon reviews provided by the Stanford Network
Analysis Project (SNAP)[15] and shared by Xiang Zhang [25]. In this dataset
an user gives scores for products in the range of one to ve starts. We associated
the scores with negative sentiments(anger,disgust, sadness,fear) and positive sen-
timents(joy, trust, anticipation, surprise) and a polarity value. The gure 3)
presents the evaluation using polarity and sentiment with automatic and man-
ual similarity measures during the semantic processing (polaritySemRelAuto,
polaritySemRelManual, SSRelAuto and SSRelManual) and PolarityLexical(base
line).

Fig. 3. Evaluation in amazon reviews

Additionally the gure 4 presents the evaluation with precision for the dis-
ambiguation process using polarity with automatic and manual similarity mea-
sures (polarityAuto, polarityManual). The results were compared to polarity
lexical(baseline) with random sense selection (PolarityLexicalR1-R10).
Knowledge-based Identication of Emotional Status on Social Networks 9

Fig. 4. Evaluation of disambiguation

5.2 Sentiments evaluation on Twitter
For this evaluation some comments were retrieved from Twitter and manually
associated with a sentiment and polarity. The gure 5 presents the results only
considering precision. The PrecisionLex (baseline) was calculated using only po-
larity. On the other hand PrecisionSS considered sentiment and computed a
semantic analysis and a disambiguation process. In this experiment the Preci-
sionSS presented better results.

Fig. 5. Evaluation Twitter

During the experiments we noticed that the methodology provides dierent
results for specic sentiments (gure 6). For instance the sentiment anger or dis-
gust performed better precision because usually the comments are more explicit.
10 Julio Vizcarra et al.

On the other hand the joy was more complicated to identify because the usage
of sarcasm or more implicit sentiments in the comments.

Fig. 6. Evaluation four sentiments

6 Conclusions
In this paper a content-based methodology was proposed for the polarity calcu-
lation and sentiment status identication. The novelty of the presented work is
the capability of handling the comments as excerpts of knowledge. We provided
a mechanism of semantic processing using knowledge graphs, graph theory algo-
rithms, semantic similarities and disambiguation. For the sentiment identica-
tion our work explored three dierent approaches (polarity, sentiment, sentiment-
polarity hybrid) where the sentiment-polarity processing presented the best re-
sults.
We performed several experiments in order to compared our contribution
with the traditional method of just looking up concepts in dictionaries(baseline)
that usually counts polarity or concepts related with sentimental information
and assigns averages.
Based on the experimental analysis the best relation precision and computing
consumption was presented by the combination of sentiment, manual weights
in semantic processing and disambiguation (SSRelManual). On the other the
highest precision was obtained with automatic weights (SSRelAuto) costing a
signicant increment in the usage of computing resources. Despite of the disam-
biguation presented a slightly better precision it provided the best combination
of concepts for the construction of formal representations and thus better senti-
ment identication. The results obtained in the present work can be consulted
at the github site: https://github.com/samscarlet/SBA.
Knowledge-based Identication of Emotional Status on Social Networks 11

7 Acknowledgments

This work was supported by CONACYT and JSPS KAKENHI Grant Number
JP17H01789.

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