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C. Lee et al. (Eds.): STA 2011 Workshops, CCIS 187, pp. 5055, 2011.

Springer-Verlag Berlin Heidelberg 2011

Opinion Mining in MapReduce Framework

Kyung Soo Cho, Ji Yeon Lim, Jae Yeol Yoon, Young Hee Kim,

Seung Kwan Kim, and Ung Mo Kim

School of Information and Communication Engineering SungKyunKwan University,

2nd Engineering Building 27039 CheonCheon-Dong, JangAn-Gu,

Suwon 440-746, Republic of Korea

kisschks@hotmail.com, {01039374479,vntlffl}@naver.com,

younghees@gmail.com, libertas@korea.kr, umkim@ece.skku.ac.kr

Abstract.Presently, many researching fields are crossed and mashed up to each

fields, however, some of computer science fields cannot be solved by technique

only. Opinion mining sometimes needs a solution from other fields, too. For

example, we use a method from psychology to gain information from text about

users. Likewise, we suggested a new method of opinion mining which is using

MapReduce before, and this method also uses a WordMap which is dictionary-

like. WordMap just has information of category and value of word. If we use a

novel method of Opinion mining, it could be mining opinion from web more

powerful than before. Therefore, for stronger opinion mining, we suggest a

framework of Opinion mining in MapReduce.

Keywords: Framework, Opinion mining, WordMap, POS tagging, MapReduce.

1 Introduction

Opinion mining and Semantic web techniques are fascinating domain of searching

engine. Between them, Opinion Mining is one of the mining techniques, extracts

estimation from the internet, analyzes it, and puts out the results. These results areusable and useful in many areas like marketing or product reviews. Nonetheless,

current methods are inefficient and use time too much for huge data because they run

on a single node to process. To settle this problematic, cloud computing, which is the

center of attention for next computing environment, is appropriate. MapReduce,

which is one of cloud computing methods, already be used in Google file system.

Therefore, this paper suggests Opinion Mining in MapReduce framework to this

novel trial for designing under a cloud computing environment, and we look forward

to the framework showing performance moderately. This framework is able to be

utilized when a developer who has wanted for some object and expectations about

performance makes Opinion mining tools in MapReduce.This paper is composed as follows: in the section 2, we explain a technique of

opinion mining and existing representative research which has relation with the

framework. In the section 3, we present the framework of opinion mining in

MapReduce function. In the section 4, we finish the paper with a conclusion and our

future work.

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Opinion Mining in MapReduce Framework 51

2 Related Work

2.1 Opinion Mining Methods

Opinion mining study has been gradually growing since the late 90s. Known assentiment classification, Opinion Mining focuses not on the topic, but a users mental

attitude that topic. In late years, opinion mining has been applied to product reviews,

or other commercial things. [1] WY. Kim and others suggest a method for opinion

mining of product reviews using association rules. [2] Opinion mining field also

includes featured-based opinion mining, summarization, comparative sentence,

relation mining, opinion searching, opinion spamming, and the linguistic resource

defining & constructing. [3] [4]

In a case of sentiment classification, reading text and analyzing make a result like

, and is similarly to MapReduces [5] data structure. Sosentiment classification has a lot possibility of well-matching within MapReduce. In

addition, some rules for analysis, which is like the POS tagging technique [6] [7], or

dictionary information are usable, too.

Fig. 1.Example of sentiment classification

Figure 1 shows sentiment classification. Sentiment classification is simple concept.

It selects the sentiment of a portion of a document set Positive or Negative. If a blog

user write Hyundai is good, it will calculate and make a result positive on Hyundai.

A topic associated word is realized as important in a technique of Topic-based

classification; however, it is as insignificant in sentiment classification. The late

research on sentiment classification is mainly performed in a document level, which

can find a detailed attribute. Sentence-level studies are also being done. B Pang, and L

Lee introduce the ways of discovering sentiment in mining. [8] SM Kim and E Hovy

suggest the way of recognizing opinion and sentiment of each opinion in a given topicusing sentiment classification of sentence level, [9] It describes that opinions are

categorized by the technique of POS tagging(Part-of-speech tagging). Some

academics have focused on comments with emoticons. For instance, Potthast and S.

Becker give a method of opinion summarization of web comments [10], and J. Read

introduces a relationship between emoticons and sentiment classification, and

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52 K.S. Cho et al.

recognizes an emoticon-trained classifiers. [11] Opinion holder means a person or a

group who makes an opinion in analyzed resource. A considering of opinion holder is

central in opinion mining. Thus, SM Kim, and E Hovy submit a paper of a technique

of mining opinions generated by an opinion holder on topics in online news media

texts also. [12] Along with sentiment classification research, methods of weight forsentiment information also have studied. We suggest reader weight and method of

using LIWC[13], before.

2.2 MapReduce

Google suggest MapReduce for analyzing large data, and they use it in their BigTable

[8]. MapReduce is very simple and strong for huge size data like terabytes or

petabytes, and it is able to customize to each systems efficiently. For these reason,

MapReduce is paid attention from many researchers.

Fig. 2.MapReduce

Figure 2 shows how MapReduce implements. A master node controls all of

Worker nodes which are called by Map or Reduce nodes, and Map nodes make

intermediate data which structure is , and Reduce nodes collect

intermediate data and transform to . It is just general

fact, and it will change for each systems.

Some researchers in field of mining have attention to this function. They consider

that it will make methods of mining stronger. Kelvin Cardona, Jimmy Secretan,

Michael Georgiopoulos and Georgios Anagnostopoulos suggest a grid based system

for data mining using MapReduce. [15] In addition, Bayir, M.A, Toroslu, I.H, Cosar,

A, and Fidan, G suggest a smart miner: a new framework for mining large scale web

usage data. [16] They suggest novel methods using data mining and MapReduce. Xia,

T suggest SMS mining with MapReduce which is Large-scale sms messages miningbased on map-reduce in a Computational Intelligence and Design. [17] In their

thesis, performance of mining methods improves because of MapReduce. This mean

is MapReduce is suitable for Mining technique, and it is also adoptable to opinion

mining. We suggest a method of Using WordMap and Score-based weight in opinion

mining with Mapreduce before. [18]

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Opinion Mining in MapReduce Framework 53

3 Opinion Mining in MapReduce Framework

3.1 WordMap

A paper of Using WordMap and Score-based weight in opinion mining withMapreduce gives a structure of WordMap. It is multidimensional indexing, dictionary

data, and usable to any systems flexibly. It is possible that a developer uses this

concept on his system, and changes its element like mean or value of words. Also, it

can use additional weight policy. For example, LIWC or leader weight can use in

connected WordMap. WordMap is able to choose two ways, and the first is that the

WordMap includes supplementary weight information, and the second is linking

weight information externally. Including weight information is faster than an external

way, however, adding weight information to completed WordMap will require a lot of

time and data space.

3.2 RuleBox

RuleBox is a part to classify sentiment and analyze in the opinion mining in

MapReduce framework. It can connected additional component like as POS tagging

technique. It defines a sentence or document with using WordMap part. A system

developer is always able to choose and customize reasonable rules in RuleBox for

appropriate his systems; however, RuleBox must have one or more rules like a natural

language processing method. The POS tagging, we mentioned, is a representative

natural language processing method.

3.3 Framework

Fig. 3. Opinion mining in MapReduce framework

Figure 3 shows Opinion mining in MapReduce framework. This framework has

three parts: MapReduce, WordMap, and RuleBox. The MapReduce part is membraneof the framework, the RuleBox part is brain, and the WordMap part is resource for the

framework.

The WordMap and RuleBox influence to accuracy of opinion mining, and

MapReduce improves time performance of the framework. [16][18] The Opinion

mining in MapReduce framework can use for searching engines and it will make

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54 K.S. Cho et al.

searching results wealthier. Also it is able to use strong marketing analyzing tools in

companies for collecting their product reviews, and government is able to utilize this

framework for their information gathering and analysis. For example, In case of

America, Google and CIA make co-financing investment company which called

recorded futures. This company uses mining methods with a technique of huge dataprocessing. This fact is issued in several newspapers.

4 Conclusion and Future Work

We suggest an opinion mining in MapReduce framwork. It is novel method of

opinion mining technique and using MapReduce. This framework is useful to

someone who wants to develop opinion mining in MapReduce, however, it is

unsuitable for small size data because the construction of WordMap part spends a lot

of time cost, and it is inefficient that several nodes analyze small size data.

Nonetheless, it is powerful for large scale data, and has a strong point of flexibility.

Today, many companies want to know opinion of their products in the internet,

therefore, opinion mining which analyze huge resource is interesting research topic.

Next task is to improve performance and accuracy of opinion mining technique in

MapReduce.

Acknowledgments.This work was supported by the Korea Science and Engineering

Foundation (KOSEF) grant funded by the Korea government (MEST) (No. 2009-

0075771).

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