Opinion Mining using TRC Techniques

Nirach Romyen, Sureeporn Nualnim, Maleerat Maliyaem, Pudsadee Boonrawd,

Kanchana Viriyapant and Tongpool Heeptaisong

Information Technology Program, King Mongkut’s University of Technology North Bangkok,

1518 Pracharat 1 Road, Bangkok, Thailand

Keywords: Sentiment Analysis, Opinion Mining, Text Representing Centroids, Co-occurrence Graph.

Abstract: Sentiment analysis is a recent research field in Natural Language Processing (NLP). Text mining and

computational techniques determine the sentiment discovered from text. This paper proposes a sentiment

analysis using the Text-Representing Centroid (TRC). TRC is a method to determine minimum average

distance to all words of the respective document, it also deploys a co-occurrence graph to represent existing

relationships among terms in a customer’s reviews on particular products and services. A corpus that contains

800 randomly selected hotel reviews from TripAdvisor website is used to evaluate performance by

comparison between TRC method and expert’s judgment review. The results show 75% accuracy over Thai

customer’s reviews.

1 INTRODUCTION

For any business, studying customer feedback has

become significant to the business because their

opinion about the experience they have with a product

or service is a resource for improving the customer

experience and adjusting business actions to their

needs. Moreover, there are also many customer

reviews available on the internet, such as websites,

blogs, forums, and social media have become a

necessary resource for obtaining information related to

any topic or domain because of allowing people to provide

their opinion about the services and products (Chumwatana,

2015; Juarez, Cervantes Villagómez, & Ayala, 2018).

Given the amount of text produced by interactions on

websites, blogs, forums, and social media, etc., is important

to use advanced techniques to be able to understand and

obtain valuable patterns from these large volumes of data

(Bouadjenek, Hacid, & Bouzeghoub, 2016).

Sentiment analysis techniques have become an

important task in recent years. Sentiment analysis,

also called opinion mining is one of the research topic

in the area of natural language processing (NLP), text

mining and computational techniques. It plays a

significant role in our decision-making process to

automate the extraction or classification of sentiment

from sentiment reviews (Abirami & Gayathri, 2017).

The bag-of-words method, which is a traditional

method for opinion mining, extracts review into many

words, and uses extracted words to identify whether

the opinion polarity is positive or negative feedbacks

(Chumwatana, 2015). This method is appropriate for

capturing word frequency, however structure and

information are ignored (Sonawane & Kulkarni,

2014).

In addition, the major problems that should not be

ignored in the use of bag-of-words with Thai

customer’s review are: Thai words are written next to

each other, without separate words and special

character to identify the end of sentence like in English

(Chumwatana, 2015). Graph techniques which

mathematically constructs model of relationships and

structural information effectively is a growing area of

study. This technique is powerful because it can be

helpful in the text that yields novel and insightful

knowledge from data (Sonawane & Kulkarni, 2014).

Sentiment analysis of Thai reviews is a complex

task because of Thai language writing style, a Thai

sentence starts from left to right without spaces

between words and capital letters, the order of words

in a sentence also plays a role in determining the part

of speech. In Thai language, some words may have

many meanings and derivational suffix used for

creating a new word from the base word. Text

centroid is an investigated method recently used to

categorize and compare documents written in

European languages (Mario M Kubek & Herwig

Unger, 2016). Moreover, the text-representing

centroids method can be applied to Thai documents,

too (Nualnim, Romyen, & Sodanil, 2019).

Romyen, N., Nualnim, S., Maliyaem, M., Boonrawd, P., Viriyapant, K. and Heeptaisong, T.

Opinion Mining using TRC Techniques.

DOI: 10.5220/0010315203210326

In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021), pages 321-326

ISBN: 978-989-758-486-2

321

Therefore, this paper proposes a sentiment

analysis of Thai customer' reviews using a text-

representing centroid method. On the methodology of

centroid terms, this method compares the distance

between the centroid term of reviews and the

reference centroid term in the corpus. This paper

applies co-occurrence graphs to determine centroid

terms of text documents because it obtains more

information about the text documents than word

frequency (Nualnim et al., 2019).

The rest of this paper is organized as follows:

Section 2, presents the base theories and methods. A

methodology is described in section 3. Section 4,

covers experiments and results. Finally, section 5,

conclusions the paper.

2 RALATED WORK

2.1 Sentiment Analysis

Sentiment analysis (or) opinion mining has gained a

lot of interest in text mining and plays a significant

role in our daily decision making process (Abirami &

Gayathri, 2017; Haruechaiyasak, Kongthon,

Palingoon, & Trakultaweekoon, 2018). It is the

automated process of understanding an opinion about

a given subject from a written language

(Krishnakumari & Akshaya, 2019). The task of

sentiment analysis consists of determining the

polarity associated with a given text document

(Castillo, Cervantes, & Vilarino, 2017). Thai

sentiment analysis is a complex task because Thai is

one of unsegmented languages (Porntrakoon &

Moemeng, 2018). For Thai language writing style, a

sentence starts from the left to right without capital

letters, with no space between words or markers at the

end of the sentence. The order of the words in the

sentence determines the role and meaning of the

words. In Thai language some words may have many

meanings and derivational suffix used for creating a

new word from the base word. Research on the

analysis of sentiment has already been proposed, such

as S-Sense (Haruechaiyasak et al., 2018). SenseComp

(Porntrakoon & Moemeng, 2018), and SenseTag

(Trakultaweekoon & Klaithin, 2016).

The work by Haruechaiyasak et al. (2018)

proposed a framework called S-sense for analyzing

sentiment on Thai social media. The framework

involves two main components, analysis modules,

and language resources. The analysis modules,

consist of two modules such as intention and

sentiment. They are based on a classification

algorithm to automatically assign appropriate

intention and sentiment class labels for a given text.

The language resources are used to train classification

models i.e. corpus and lexicon. Porntrakoon and

Moemeng (2018) proposed a framework called

SenseComp to automatically analyze Thai sentiment

of consumer’s review in product, price, and shipping

dimensions. The methodology of framework use

multi-dimensional lexicon and sentiment

compensation technique. Sentiment compensation

technique is used to automatically compensate the

sentiment to a dimension where consumer’s review

mentioned without a dimension. Additional sentiment

analysis for Thai natural language processing is

proposed by Chumwatana (2015). This study aims to

analyses Thai customers’ opinions from the

comments on social media. The method combines

Thai word extraction technique and sentiment

analysis technique to check customers’ opinions, in

order to classify customers’ comments into three

categories: neutral, positive and negative. The

research process consists of five main steps: (1)

collecting Thai customers’ comments from social

media, (2) Thai word extraction, (3) detecting polarity

words, (4) calculating polarity word scores from

comments and (5) classifying the comments into

groups.

2.2 Text Centroid Representation

Text centroid is a method to identify the representative

of the word in the documents that have been inspired

by the center of mass in physics. It is the arithmetic

mean of all points weighted by the local density as

depicted in Figure 1 (Mario M. Kubek & Herwig

Unger, 2016). If a physical object has uniform

density, its center of mass is the same as the centroid

of its shape.

Figure 1: The center of mass in physics.

The distribution of mass is balanced around the

center and the average of the weighted coordinates of

the distributed mass determines the coordinates and

position of the object. To reduce the complexity of

calculations, normally this will be replaced with a

single mass at the position or the center of mass

ICPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods

322

(Mario M. Kubek & Herwig Unger, 2016). When

compared to text processing, mass point will mean

word and distance vector will mean distance in co-

occurrence graph.

The centroid of a document is the term with the

minimum average distance to all words of the

respective document in the co-occurrence graph. This

is illustrated in Figure 2.

Figure 2: Words of the document in the co-occ. graph.

Consider Figure 2, when you need to find a

centroid term of the document consists of the word

school, classroom, student, teacher, and computer, the

process of finding the centroids is as follows: the word

"school" will be the centroid of this document because

it is the term with the minimum average distance to

another word in the document.

Text-representing centroid is a basic component

of the distance-based measure. The co-occurrence

graph will be used to determine centroid terms of text

documents. The reason for choosing is that the co-

occurrence graph obtains more detailed information

about the text documents than term frequency

vectors. Consequently, it can reflect text structure

information and retain important semantic

information (Mario M. Kubek & Herwig Unger,

2016).

co-occurrence graph, terms are represented by

vertices and relation between terms is represented by

edges. The co-occurrence graphs 𝐺 



𝑊,𝐸



could

be obtained, if all words of a document or a set of

documents 𝑊 are used to build its set of nodes which

are then connected by an edge



𝑤



,𝑤





∈𝐸 if 𝑤



∈

𝑊 and 𝑤



∈𝑊 are co-occurrences. Therefore, any

two words 𝑤



and 𝑤



are called co-occurrence,

which is connected by an edge



𝑤



,𝑤





∈𝐸, if they

appear together in one sentence. The centroid term

can be computed by the following steps:

Step 1: Calculate weight function g((

𝑤



,𝑤



)).

Weight function is the number of occurrences of

the two words together in a sentence. If g((

𝑤



,𝑤



))

is high, that means two words are semantically

close (Mario M. Kubek & Herwig Unger, 2016).

Thus, the following equation is obtained:

𝑔



𝑤



,𝑤





 

𝑓

𝑟𝑒𝑞



𝑤



,𝑤





𝑓

𝑟𝑒𝑞



𝑤







𝑓

𝑟𝑒𝑞



𝑤







𝑓

𝑟𝑒𝑞



𝑤



,𝑤





(1)

Where g((

𝑤



,𝑤



)) denotes the weight between

node 𝑤



and 𝑤



, 𝑓𝑟𝑒𝑞



𝑤



,𝑤





shows the number

of times 𝑤



and 𝑤



occur together in the unit.

𝑓𝑟𝑒𝑞



𝑤





and

𝑓𝑟𝑒𝑞



𝑤





denotes the frequency of

𝑤



and 𝑤



appearing in the documents respectively.

Step 2: Calculate distance 𝑑



𝑤



,𝑤





between two

words from graph G which can be defined by:

𝑑



𝑤



,𝑤







𝑔



𝑤



,𝑤





(2)

In all other cases, there is a shortest path 𝑝 



𝑤



,𝑤







𝑤



,𝑤





,..,



𝑤



,𝑤





with 𝑤



 𝑤



,𝑤





𝑤



and 𝑤



,𝑤



∈𝐸 for all 𝑖 1





𝑘 such that

𝑑



𝑤



,𝑤





𝑑



𝑤



,𝑤





 𝑀𝐼𝑁





(3)

Whereby in case of a partially connected co-

occurrence graph

𝑑



𝑤



,𝑤





∞

must be set.

Step 3: Calculate centroid-term of documents 𝐷

This can be calculated by the distance between a given

term 𝑡 and documents 𝐷 containing 𝑁 words

𝑁𝑤



,𝑁𝑤



,… ,𝑁𝑤



∈𝐷 that are reachable from 𝑡 in 𝐺

which can be defined by

𝑑



𝐷,𝑡





∑

𝑑𝑁𝑤

,

𝑡





𝑁

(4)

3 RESEARCH METHODOLOGY

In this paper, the proposed method aims to check Thai

customer’s opinions from the comments using the

methodology of text-representing centroids to classify

the polarity of text as positive or negative. The

proposed techniques are based on a co-occurrence

graph that represents existing relationships among

terms in a customer’s reviews or opinions about the

products and services. Its detailed procedure is

presented in Figure 3. The overview of the research

process of the proposed methodology, which consists

of many steps, is described below.

Opinion Mining using TRC Techniques

323

Figure 3: Process of research methodology.

3.1 Collection of Thai Customer’s

Reviews

The collection of Thai customer’s review is

considered the first process for the proposed

technique. To perform comment analysis, the

collected customers’ reviews were classified as

positive or negative by Thai language experts to

determine the polarity of the review. The Thai

customers’ reviews collected from Tripadvisor

website are then used as an input to extract Thai

words in the next step.

3.2 Data Pre-processing

Thai customers’ review collected from the first step

will be an input for the next process. The pre-

processing is a process to ensure the quality of the data

before finding the centroid terms, so the pre-

processing process consists of several steps. First step

is extracting words according to the grammars; second

step is considering the meaning of words according to

the position of the words in the sentence; the final step

is eliminating unwanted words such as conjunctions,

and verbs. Therefore, the PyThaiNLP library for

python version 3.7, which uses tokenize engine

algorithm called newmm (New Maximum Matching

algorithm), is applied to divide customer’s review into

words. After these Thai words are extracted from

reviews, the next step is negation checking. This

technique uses Parts of speech (POS) to identified

negation word, if the word “ดี (good)” follows

negation word “ไม (not)”, then combined negation

word with the following word to generate a new word

“ไมดี (not good)”. Subsequently, remove the stop

words and choose only adjectives. Therefore, the

TLTK library is applied to identify POS tagging.

3.3 Finding Reference Centroid Terms

The reference centroid terms are created to be used as

a reference point for the positive sentence and the

negative sentence. In order to find the reference

centroid terms of a Thai document, centroid terms

must be determined with the help of the undirected co-

occurrence graph G. Creating reference centroid terms

as reference point for positive and negative sentence

uses the following steps:

Step1: Create a corpus. This step creates the

corpus that contains 400 positive reviews and 400

negative reviews.

Step2: Create a co-occurrence graph. A co-

occurrence graph is created by calculating weight of

co-occurrence frequency using Formula 1 in this

paper.

Step3: Calculate distance between words. The

distance of two words in the graph is found using

Formula 2 or Formula 3.

Step4: Calculate centroids terms using Formula 4.

3.4 Creating a Centroid Corpus

The corpus used to create the reference co-occurrence

graph includes 800 customer reviews selected

randomly from Tripadvisor website. The data is

divided into 400 positive customer reviews and 400

negative customer reviews. The reviews have been

classified as positive or negative by experts.

3.5 Customer Reviews Classification

This process aims to classify the polarity of the text

to be positive or negative. The process consists of

finding centroid terms of review using Formula 1-4 in

this paper. After that, compares the distance between

the centroid term of review and the reference centroid

term in the corpus.

Furthermore, the concept of

distance measure is a way of describing what it means

for elements of some space to be "close to" or "far

away from" each other. If the distance is minimum, it

means the meaning is close to each other.

4 EXPERIMENTAL AND

RESULTS

This section describes the experiment on analyzation

of customers’ reviews based on the proposed method.

Data pre-processing

Custome

’s Reviews

Finding reference centroid

Creating a centroid corpus

Classifying the customer’s reviews

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324

The research hypothesis is that the methodology of

text-representing centroids is a good alternative to

classify the polarity of text as positive or negative.

This section presents two subsections, classifying the

polarity of text and comparing performance.

4.1 Classifying the Polarity of Text

To apply the text-representing centroid to classify the

polarity of the text as positive or negative. The

centroid terms have been determined with the help of

the co-occurrence graph in the corpus. The corpus

used to create the reference co-occurrence graph

includes 800 customer reviews selected randomly

from the TripAdvisor website. In the experiment, 200

customer reviews are used. To be able to evaluate the

effectiveness of the text-representing centroids

method, this data is chosen equally, divided into 100

positive comments and 100 negative comments from

the same data set used to create a co-occurrence graph.

For each customer reviews, find the centroids of the

customer reviews using Formula 4. Afterward,

calculate the distance between the new centroids of the

customer reviews, compare it to the reference centroid

terms in the corpus. In this experiment, the reference

point of the positive comment is a “positive term”, and

the reference point of the negative comment is a

“negative term”. The example results of finding the

polarity of comment are shown in Table 1.

Table 1: Distance from centroid term to positive and

negative term.

No Data Centroid

term

Distance

Pos. Neg.

เกา (old)

ไมคอยอรอย

(terrible)

เกา (old)

0.48 0.35

อรอย

(delicious)

วุนวาย (chaotic)

เสียงดัง(loudness)

เสียงดัง

(loudness)

0.49 0.43

ชอบ(like)

เลิศ

(excellent)

เกา (old)

ดีมาก (good)

เกา (old)

0.48 0.35

ดี (good)

อับ (smelly)

ดี (good)

0.28 0.32

สะดวก convenient

สวย (beautiful)

พลุกพลาน(busily)

เกา (old)

โทรม (shabby)

สะดวก

convenient

0.35 0.40

Table 1 presents information about centroid terms

of customer reviews and the distance between the new

centroid terms compared to a positive reference point

and a negative reference point in the corpus. In the

table, if the distance value is minimum, it means the

new centroid term is close to reference point in the

corpus. For example, according to Table 1, for the

first sentence, when calculating the text-representing

centroid, the new centroid term is "old". When

calculating the distance between the new centroid

term with the positive reference point and the negative

reference point in the corpus, it is found that the new

centroid term close to a negative reference point. That

means the first sentence is a negative opinion.

4.2 Comparing Performance

To evaluate the effectiveness of the text-representing

centroids method. The distance measure technique is used

to evaluate the accuracy of the sentiment analysis. This

technique is a comparison of accuracy between analysis

using text-representing centroids methods and customer’s

review already categorized by experts. The results are

shown in Table 2.

Table 2: The results of comparing performance.

Category Number of

sentence

Result Accuracy

percentage

Positive 100 Pos:

96 sentence

Neg:

4 sentence

Negative 100 Pos:

46 sentence

Neg:

54 sentence

The table shows information about the accuracy of

the polarity of comments, consisting of 200 comments

divided into 100 positive comments and 100 negative

comments. In Table 2, it is clear that positive

comments have a higher percentage of analytical

accuracy than negative comments. Positive comments

have an accuracy of 96 percent, while negative

comments have an accuracy of 54 percent. The reason

that positive comments has higher accuracy than

negative counterparts is because the authors give

opinions clearly and there are no negative opinions in

positive sentences.

5 CONCLUSIONS

This paper presents an approach that uses and applies

centroid based techniques on Thai reviews from

Opinion Mining using TRC Techniques

325

Tripadvisor website to analyze their sentiments. The

proposed method consists of five main processes: (1)

collection of Thai customers’ review (2) data

preprocessing (3) finding reference centroid terms (4)

creating a corpus and (5) classifying the customer

reviews. The experiment was created to compare the

accuracy between the analysis using the text-

representing centroids method and the customer’s

review already categorized by experts. The

experiment results show that the proposed methods

correctly analyzes the positive comments better than

negative comments. The positive comment

classification has an accuracy of 96 percent, while the

negative counterpart has an accuracy of 54 percent.

Considering the results for comments that are

negative, in Thai culture, reviews would begin with

positive opinions first and then express negative

opinions later. From the above reasons, the use of the

methodology of text-centroid to analyze sentiment

results yield more errors with negative sentences than

positive ones. Future work will involve improving the

accuracy of sentiment analysis by considering the

importance of previous sentences and focus on the

words found in both the positive cluster and the

negative cluster.

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