METHODOLOGICAL STUDY OF OPINION MINING

Transcription

1 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 METHODOLOGICAL STUDY OF OPINION MINING AND SENTIMENT ANALYSIS TECHNIQUES Pravesh Kumar Sngh 1, Mohd Shahd Husan 2 1 M.Tech, Department of Computer Scence and Engneerng, Integral Unversty, Lucknow, Inda 2 Assstant Professor, Department of Computer Scence and Engneerng, Integral Unversty, Lucknow, Inda ABSTRACT Decson makng both on ndvdual and organzatonal level s always accompaned by the search of other s opnon on the same. Wth tremendous establshment of opnon rch resources lke, revews, forum dscussons, blogs, mcro-blogs, Twtter etc provde a rch anthology of sentments. Ths user generated content can serve as a benefacton to market f the semantc orentatons are delberated. Opnon mnng and sentment analyss are the formalzaton for studyng and construng opnons and sentments. The dgtal ecosystem has tself paved way for use of huge volume of opnonated data recorded. Ths paper s an attempt to revew and evaluate the varous technques used for opnon and sentment analyss. KEYWORDS Opnon Mnng, Sentment Analyss, Feature Extracton Technques, Naïve Bayes Classfers, Clusterng, Support Vector Machnes 1. INTRODUCTION Generally ndvduals and companes are always nterested n other s opnon lke f someone wants to purchase a new product, then frstly, he/she tres to know the revews.e., what other people thnk about the product and based on those revews, he/she takes the decson. Smlarly, companes also excavate deep for consumer revews. Dgtal ecosystem has a plethora for same n the form of blogs, revews etc. A very basc step of opnon mnng and sentment analyss s feature extracton. Fgure 1 shows the process of opnon mnng and sentment analyss. DOI: /sc

2 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 There are varous methods used for opnon mnng and sentment analyss among whch followng are the mportant ones: 1) Naïve Bays Classfer. 2) Support Vector Machne (SVM). 3) Multlayer Perceptron. 4) Clusterng. In ths paper, categorzaton of work done for feature extracton and classfcaton n opnon mnng and sentment analyss s done. In addton to ths, performance analyss, advantages and dsadvantages of dfferent technques are apprased. 2. DATA SETS Ths secton provdes bref detals of datasets used n experments Product Revew Dataset Bltzer takes the revew of products from amazon.com whch belong to a total of 25 categores lke vdeos, toys etc. He randomly selected ve and 4000 ve revews Move Revew Dataset The move revew dataset s taken from the Pang and Lee (2004) works. It contans move revew wth feature of ve and 1000 ve processed move revews. 3. CLASSIFICATION TECHNIQUES 3.1. Naïve Bayes Classfer It s a probablstc and supervsed classfer gven by Thomas Bayes. Accordng to ths theorem, f there are two events say, e 1 and e 2 then the condtonal probablty of occurrence of event e 1 when e 2 has already occurred s gven by the followng mathematcal formula: P( e P ( e e ) = e1 ) P( e1 ) e Ths algorthm s mplemented to calculate the probablty of a data to be postve or negatve. So, condtonal probablty of a sentment s gven as: P(Sentment)P(Sentence Sentment) P (Sentment Sentence) = P(Sentence) And condtonal probablty of a word s gven as: Numberof wordoccurencen class+ 1 P(Word Sentment )= Numberof wordsbelongngto a class+totalnos of Word Algorthm 2 S1: Intalze P(postve) num popozt (postve)/ num_total_propozt 12

3 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 S2: Intalze P(negatve) num popozt (negatve) / num_total_propozt S3: Convert sentences nto words for each class of {postve, negatve}: for each word n {phrase} num_total_cuvnte P(word class) < num_apart (word class) 1 num_cuv (class) + P (class) P (class) * P (word class) Returns max {P(pos), P(neg)} The above algorthm can be represented usng fgure 2 +ve Sentence Classfer Tranng Set Classfer ve Sentence Sentence Revew Classfer Book Revew Fgure 2. Algorthm of Naïve Bayes Evaluaton of Algorthm To evaluate the algorthm followng measures are used: Accuracy Precson Recall Relevance Followng contngency table s used to calculate the varous measures. Detected Opnons Undetected Opnons Relevant True Postve (tp) False Negatve (fn) Irrelevant False Postve (fp) True Negatve (tn) Now, Precson = tp tp + fp Accuracy = tp + tn, F tp + tn + fp + fn = 2*Pr ecson *Re call Pr ecson + Re call ; Recall = tp tp + fn 13

4 Accuracy Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 On the 5000 sentences [1] Ion SMEUREANU, Crstan BUCUR tran the Naïve Gauss Algorthm and got accuracy; Where number of groups (n) s Advantages of Naïve Bayes Classfcaton Method 1. Model s easy to nterpret. 2. Effcent computaton Dsadvantage of Naïve Bayes Classfcaton Method Assumptons of attrbutes beng ndependent, whch may not be necessarly vald. 3.2 Support Vector Machne (SVM) SVM s a supervsed learnng model. Ths model s assocated wth a learnng algorthm that analyzes the data and dentfes the pattern for classfcaton. The concept of SVM algorthm s based on decson plane that defnes decson boundares. A decson plane separates group of nstances havng dfferent class membershps. For example, consder an nstance whch belongs to ether class Crcle or Damond. There s a separatng lne (fgure 3) whch defnes a boundary. At the rght sde of boundary all nstances are Crcle and at the left sde all nstances are Damond. Support Vectors Support Vectors Is there s an exercse/tranng data set D, a set of n ponts s wrtten as: D = n p {( x, c ) x ε R, c ε{ 1,1 }...(1) 1 Where, x s a p-dmensonal real vector. Fnd the maxmum-margn hyper plane.e. splts the ponts havng c = 1 from those havng c = -1. Any hyperplane can be wrtten as the set of ponts satsfyng: w x - b = 1...(2) Fgure 3. Prncple of SVM 14

5 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 Fndng a maxmum margn hyperplane, reduces to fnd the par w and b, such that the dstance between the hyperplanes s maxmal whle stll separatng the data. These hyperplanes are descrbed by: w x b=1 and w x b = 1 The dstance between two hyperplanes s mnmzed w n w, b subect to c (w.x b) 1 for any = 1 n. b and therefore w needs to be mnmzed. The w Usng Lagrange s multplers (α ) ths optmzaton problem can be expressed as: mn w, b max 1 { w n α = 1 α [c (w.x - b) -1] }...( 3) Extensons of SVM There are some extensons whch makes SVM more robust and adaptable to real world problem. These extensons nclude the followng: 1. Soft Margn Classfcaton In text classfcaton sometmes data are lnearly dvsble, for very hgh dmensonal problems and for mult-dmensonal problems data are also separable lnearly. Generally (n maxmum cases) the opnon mnng soluton s one that classfes most of the data and gnores outlers and nosy data. If a tranng set data say D cannot be separated clearly then the soluton s to have fat decson classfers and make some mstake. Mathematcally, a slack varable ξ are ntroduced that are not equal to zero whch allows x to not meet the margn requrements wth a cost.e., proportonal to ξ. 2. Non-lnear Classfcaton Non-lnear classfers are gven by the Bemhard Boser, Isabelle Guyon and Vapnk n 1992 usng kernel to max margn hyperplanes. Azeman gven a kernel trck.e., every dot product s replaced by non-lnear kernel functon. When ths case s apply then the effectveness of SVM les n the selecton of kernel and soft margn parameters. 3. Multclass SVM Bascally SVM relevant for two class tasks but for the multclass problems there s multclass SVM s avalable. In the mult class case labels are desgned to obects whch are drawn from a fnte set of numerous elements. These bnary classfers mght be bult usng two classfers: 1. Dstngushng one versus all labels and 2. Among each par of classes one versus one Accuracy When pang take ungrams learnng method then t gves the best output n a presence based frequency model run by SVM and he calculated 82.9% accuracy n the process. 15

6 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February Advantages of Support Vector Machne Method 1. Very good performance on expermental results. 2. Low dependency on data set dmensonalty Dsadvantages of Support Vector Machne Method 1. One dsadvantages of SVM s.e. n case of categorcal or mssng value t needs pre-processed. 2. Dffcult nterpretaton of resultng model Mult-Layer Perceptron (MLP) Mult-Layer perceptron s a feed forward neural network, wth one or N layers among nputs and output. Feed forward means.e, un-drecton flow of data such as from nput layer to output layer. Ths ANN whch multlayer perceptron begn wth nput layer where every node means a predcator varable. Input nodes or neurons are connected wth every neuron n next layer (named as hdden layers). The hdden layer neurons are connected to other hdden layer neuron. Output layer s made up as follows: 1. When predcton s bnary output layer made up of one neuron and 2. When predcton s non-bnary then output layer made up of N neuron. Ths arrangement makes an effcent flow of nformaton from nput layer to output layer. Fgure 4 shows the structure of MLP. In fgure 4 there s nput layer and an output layer lke sngle layer perceptron but there s also a hdden layer work n ths algorthm. MLP s a back propagaton algorthm and has two phases: Phase I: It s the forward phase where actvaton are propagated from the nput layer to output layer. Phase II: In ths phase to change the weght and bas value errors among practcal & real values and the requested nomnal value n the output layer s propagate n the backward drecton. MLP s popular technque due to the fact.e. t can act as unversal functon approxmator. MLP s a general, flexble and non-lnear tool because a back propagaton network has mnmum one hdden layer wth varous non-lnear enttes that can learn every functon or relatonshp between group of nput and output varable (whether varables are dscrete or contnuous). 16

7 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 An advantage of MLP, compare to classcal modelng method s that t does not enforce any sort of constrant wth respect to the ntal data nether does t generally start from specfc assumptons. Another beneft of the method les n ts capablty to evaluaton good models even despte the presence of nose n the analyzed nformaton, as arses when there s an exstence of omtted and outler values n the spreadng of the varables. Hence, t s a robust method when dealng wth problems of nose n the gven nformaton Accuracy On the health care data Ludmla I. Kuncheva, (IEEE Member) calculate accuracy of MLP as 84.25%-89.50% Advantages of MLP 1. It acts as a unversal functon approxmator. 2. MLP can learn each and every relatonshp among nput and output varables Dsadvantages of MLP 1. MLP needs more tme for executon compare to other technque because flexblty les n the need to have enough tranng data. 2. It s consdered as complex black box. 3.4 Clusterng Classfer Clusterng s an unsupervsed learnng method and has no labels on any pont. Clusterng technque recognzes the structure n data and group, based on how nearby they are to one another. So, clusterng s process of organzng obects and nstances n a class or group whose members are smlar n some way and members of class or cluster s not smlar to those are n the other cluster Ths method s an unsupervsed method, so one does not know that how many clusters or groups are exstng n the data. Usng ths method one can organze the data set nto dfferent clusters based on the smlartes and dstance among data ponts. 17

8 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 Clusterng organzaton s denoted as a set of subsets C = C 1... C k of S, such that: k S= C and C C = φ for. Therefore, any obect n S related to exactly one and only one subset. = 1 For example, consder fgure 5 where data set has three normal clusters. Now consder the some real-lfe examples for llustratng clusterng: Example 1: Consder the people havng smlar sze together to make small and large shrts. 1. Talor-made for each person: expensve 2. One-sze-fts-all: does not ft all. Example 2: In advertsng, segment consumers accordng to ther smlartes: To do targeted advertsng. Example 3: To create a topc herarchy, we can take a group of text and organze those texts accordng to ther content matches. Bascally there are two types of measures used to estmate the relaton: Dstance measures and smlarty measures. Bascally followng are two knds of measures used to guesstmate ths relaton: 1. Dstance measures and 2. Smlarty measures Dstance Measures To get the smlarty and dfference between the group of obects dstance measures uses the varous clusterng methods. It s convenent to represent the dstance between two nstances let say x and x as: d (x, x ). A vald dstance measure should be symmetrc and gans ts mnmum value (usually zero) n case of dentcal vectors. If dstance measure follows the followng propertes then t s known as metrc dstance measure: 1.Tranglenequaltyd(x,x ) d(x,x )+d(x,x ) 2.d(x,x )= 0 x = x k x,x,x x,x k S S There are varatons n dstance measures dependng upon the attrbute n queston Clusterng Algorthms A number of clusterng algorthms are gettng popular. The basc reason of a number of clusterng methods s that cluster s not accurately defned (Estvll -Castro, 2000). As a result many clusterng methods have been developed, usng a dfferent nducton prncple. 1. Exclusve Clusterng In ths clusterng algorthm, data are clusters n an exclusve way, so that a data fts to only one certan cluster. Example of exclusve clusterng s K-means clusterng. k 18

9 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February Overlappng Clusterng Ths clusterng algorthm uses fuzzy sets to grouped data, so each pont may ft to two or more groups or cluster wth varous degree of membershp. 3. Herarchcal Clusterng Herarchcal clusterng has two varatons: agglomeratve and dvsve clusterng Agglomeratve clusterng s based on the unon among the two nearest groups. The start state s realzed by settng every data as a group or cluster. After some teraton t gets the fnal clusters needed. It s a bottom-up verson. Dvsve clusterng begns from one group or cluster contanng all data tems. At every step, clusters are successvely fragmented nto smaller groups or clusters accordng to some dfference. It s a top-down verson. 4. Probablstc Clusterng It s a mx of Gaussan, and uses totally a probablstc approach Evaluaton Crtera Measures for Clusterng Technque Bascally, t s dvded nto two group s nternal qualty crtera and external qualty crtera. 1. Internal Qualty Crtera Usng smlarty measure t measures the compactness f clusters. It generally takes nto consderaton ntra-cluster homogenety, the nter-cluster separablty or a combnaton of these two. It doesn t use any exteror nformaton besde the data tself. 2. External Qualty Crtera External qualty crtera are mportant for observng the structure of the cluster match to some prevously defned classfcaton of the nstance or obects Accuracy Dependng on the data accuracy of the clusterng technques vared from 65.33% to 99.57% Advantages of Clusterng Method The most mportant beneft of ths technque s that t offers the classes or groups that fulfll (approxmately) an optmalty measure Dsadvantages of Clusterng Method 1. There s no learnng set of labeled observatons. 2. Number of groups s usually unknown. 3. Implctly, users already choose the approprate features and dstance measure. 4. CONCLUSION The mportant part of gatherng nformaton always seems as, what the people thnk. The rsng accessblty of opnon rch resources such as onlne analyss webstes and blogs means that, one can smply search and recognze the opnons of others. One can precse hs/her deas and opnons concernng goods and facltes. These vews and thoughts are subectve fgures whch sgnfy opnons, sentments, emotonal state or evaluaton of someone. 19

10 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 In ths paper, dfferent methods for data (feature or text) extracton are presented. Every method has some benefts and lmtatons and one can use these methods accordng to the stuaton for feature and text extracton. Based on the survey we can fnd the accuracy of dfferent methods n dfferent data set usng N-gram feature shown n table 1. Table 1: Accuracy of Dfferent Methods N-gram Feature Move Revews Product Revews NB MLP SVM NB MLP SVM Accordng to the survey, accuracy of SVM s better than other three methods when N-gram feature was used. The four methods dscussed n the paper are actually applcable n dfferent areas lke clusterng s appled n move revews and SVM technques s appled n bologcal revews & analyss. Although the feld of opnon mnng s new, but stll dverse methods avalable to provde a way to mplement these methods n varous programmng languages lke PHP, Python etc. wth an outcome of nnumerable applcatons. From a convergent pont of vew Naïve Bayes s best sutable for textual classfcaton, clusterng for consumer servces and SVM for bologcal readng and nterpretaton. ACKNOWLEDGEMENTS Every good wrtng requres the help and support of many people for t to be truly good. I would take the opportunty of thankng all those who extended a helpng hand whenever I needed one. I offer my heartfelt grattude to Mr. Mohd. Shahd Husan, who encouraged, guded and helped me a lot n the proect. I extent my thanks to Mss. Ratna Sngh (fancee) for her ncandescent help to complete ths paper. A vote of thanks to my famly for ther moral and emotonal support. Above all utmost thanks to the Almghty God for the dvne nterventon n ths academc endeavor. REFERENCES [1] Ion SMEUREANU, Crstan BUCUR, Applyng Supervsed Opnon Mnng Technques on Onlne User Revews, Informatca Economcă vol. 16, no. 2/2012. [2] Bo Pang and Lllan Lee, Opnon Mnng and Sentment Analyss, Foundatons and TrendsR_ n Informaton Retreval Vol. 2, Nos. 1 2 (2008). [3] Abbas, Affect ntensty analyss of dark web forums, n Proceedngs of Intellgence and Securty Informatcs (ISI), pp , [4] K. Dave, S. Lawrence & D. Pennock. \Mnng the Peanut Gallery: Opnon Extracton and Semantc Class_caton of Product Revews." Proceedngs of the 12th Internatonal Conference on World Wde Web, pp , [5] B. Lu. \Web Data Mnng: Explorng hyperlnks, contents, and usage data," Opnon Mnng. Sprnger, [6] B. Pang & L. Lee, \Seeng stars: Explotng class relatonshps for sentment categorzaton wth respect to ratng scales." Proceedngs of the Assocaton for Computatonal Lngustcs (ACL), pp ,2005. [7] Nlesh M. Shelke, Shrnwas Deshpande, Vlas Thakre, Survey of Technques for Opnon Mnng, Internatonal Journal of Computer Applcatons ( ) Volume 57 No.13, November

11 Internatonal Journal on Soft Computng (IJSC) Vol. 5, No. 1, February 2014 [8] Ndh Mshra and C K Jha, Classfcaton of Opnon Mnng Technques, Internatonal Journal of Computer Applcatons 56 (13):1-6, October 2012, Publshed by Foundaton of Computer Scence, New York, USA. [9] Oded Z. Mamon, Lor Rokach, Data Mnng and Knowledge Dscovery Handbook Sprnger, [10] Bo Pang, Lllan Lee, and Shvakumar Vathyanathan. Sentment classfcaton usng machne learnng technques. In Proceedngs of the 2002 Conference on Emprcal Methods n Natural Language Processng (EMNLP), pages [11] Towards Enhanced Opnon Classfcaton usng NLP Technques, IJCNLP 2011, pages , Chang Ma, Thaland, November 13, 2011 Author Pravesh Kumar Sngh s a fne blend of strong scentfc orentaton and edtng. He s a Computer Scence (Bachel or n Technology) graduate from a renowned gurukul n Inda called Dr. Ram Manohar Loha Awadh Unversty wth excellence not only n academcs but also had flagshp n choreography. He mastered n Computer Scence and Engneerng from Integral Unversty, Lucknow, Inda. Currently he s actng as Head MCA (Master n Computer Applcatons) department n Thakur Publcatons and also workng n the capacty of Senor Edtor. 21