INFORMATIONISSN 1343‑4500″
ヽolurne 26, Number 3,pp l17‑162

eISSN 1344‑8994C)2023 1nternational lnbrmation lnstituteSentiment Analysis using a CNN‐ BiLSTM Deep Model Based on Attention
Classiflcatiom

WANG Yue″gC力 F″ αg ИθBθ 夕″
α滋 ″ンげ「Иri′C′ α′ル″〃なθ′ει(B鋤 ,3θ グ′Lei LI

43″αlι ια″′E4g″ θ″ノCrα 滋″″ Scみ οο′a/Sθ ノ

υ物ルιrsゎ 4 KOgα″θちrbり O f8ィ ̲85∂ イ説ψα″̀ノE‐ mail.lilei@hosei・acjp

Abstract
With ie rapid developmcnt of the lntemet, the nmber of social media and c‐ comlncrce platforms increased
dralnatically.1」 scrs from al1 0ver world share their ooIIIments and scntllnents on the lnternct become a ne、v tradition.

Applying natwal language proccssing technololy to anaサ zc the te離 on the hternet for mining the emotional tendellcies
has bccomc thc main way in the social public opinion monttoring and the ater‐sale feedback of manufactory.Thus,thestudy on txt sentiment analysis has showll mportant social sig741iiCance and commercia value.Scntiment analysis is a
hot research toplc in the fleld of nattral languagc processmg and data mmmg m reccnt ten years.The paper starts withthe topic of̀̀Sentiment Allalysis using a CNN‐ BILSTM deep model bascd on attention mcchanism classincatiOn''.FIst,
it conducts an in… depth investigation on the current research stams and collllmonly used algorithms at home and abroad,and brieay intrOduccs and allaけ zes the cllrrent mainstream sentunent analysis methods.As a direction ofmachme leamulg9
deep learning has become a hot research topic m emotion classiflcation in the ield of namal language processing.nis

paper uses dcep learning models to stu島

/the sentimcnt classiflcation problem of short alld long text sentllnent

classiflcation tasks. ■ e nlain rescarch contents are as follows. Firstly, Traditional nellral ne● vork based ttort textclassiication a180Hthms for sentmlent classiflcation is easy to fmd the errors.The featurc dimension is too high,and thc
feame infbFluation ofthe pool layer is lost,、 ″hich leads to the loss ofthe details ofthe emotion vocabulary.To solve thisproblem,the Word Vector Model(Word2vec),BidrcctiOnal Long‐

tellll and Shorttellll Mcmory netwotts oiLSTM)

and convolutional neural ne柳 /ork(CNN)are cOmbined in QuOra dataset.The eゃ erlment shows that the accuracy ofCNN‐BILSTM modcl associatcd with Word2vec word embedding achieved 91.48%.This proves that thc"brid ne口 ork
model pcrfoms better than the single smcture nellral nctwork in shorttext.Convolutional neurai network(CblN)modelS
usc convolutional layers and maxunum pooling or lnax‐ overtime p∞ ling laycrs to extmct highcr… lcvel feamК s,while
LSTM Inodels can capture long―tellll dependcncies be● veen words hence are better used for text classiflcation.However,even with the hybrid approach that leverages the powcrs of thesc two deep‐

leaming models,the numbcr of taturcs toall attention

remember for classiflcation remains hugc,hence hindering the training process.Secondly7,we propose

based

CNN‐ BiLSTM

hybrid

model

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thatcapitalizeontheWANG YUE AND LEILI

advantages ofLSTh4 and CNN with an additional attention mechanisln in IPEDB movie reviews dataset,In thc cxpcHment,llllder the control ofsinglc variable ofDtta volume and Epoch,thc proposed httrid mOdcl was colnpared with the results ofvarious indicators including recall,precision,Fl scorc and accuracy ofC■ lN,LSTM and C■ lN‐ LSTM inlongtc離 .恥 Ъen山data siК was 13 k,thc proposed model had the highcst accllracy江0.908,and the Fl score also showed thc highest

perfoll・ lance at O.883.When the epoch valuc for obtaining the optirnal accllracy of cach model l〃as 10 for CNN, 14 for

LSTヽ 1,5 for NILP and 15 cpOchs for CblN‐ LSTⅣl,which took the longest leanling tilne.The Fl score also showcd thc
best perfomance ofthe proposed model江0.906,and accuracy ofthe proposed model was the highest at O.929.Finalけ,theexpcrimental results show that the bidttectional long… and short― tellll mcmory convolutional ncural ne"/ork(BiLSTM‐ChIN)model based on・ attention mechanism can dttct市 eけ imprOve the perお mance of sent口 lent classiication of data
scts when processing long‐ text sentunent classiication tasks.

Keywords:sentiment analysis,CblN,BiLSTNII,attention lnechanisllll,tcxt CLssiflcation

1. Introduction

l.l BackgroundWitll the rapid development ofthe lntemet,tlle number ofllltemet users increases sharpン .Ⅵ hen peOple aresuflng the lntemet,a large amount of infolillation is generated,v汁 五ch reflects people's views and attitudes,
alld contains great cOnlmercial alld social vallle[1].TeXt iS Still all impo■ allt wり fOr pcople to prOduce alldobtain infomation. The emotional classiflcation of the short text is conducive to cOmplete the user's push
seⅣ icc,itnprove user experience better.
In recent years,thc analysis oftext affective tendency has attacted the attention of many scholars and has

bccome a hot topic.Witll the五 se of neural nettork,■ s related algo五 Jtts show a higher classiicatiOn efLctin text classiflcation.The emotiOnal detection and classiflcation of text wOrds,sentences and dOculnents to agroup of emotiOns by using pwchological models.Therefore,a great practical signincance to allalyze thc
emotional infomation contained in the text and classitt the text emotionally.

Sentiment analysis[2]is a set of linguistic operations belonging to the automatic pЮ

cessing of naturallanguage.■ 's otteCtiVe to idelltitt the SCntiment eマ ressed in the text and to predict its polarity(pOSitive ornegative)tOWards a g市 en sutteCt[3].

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