Hybrid Deep Learning Model for Multilingual Sentiment Analysis by IRJET Journal

Hybrid Deep Learning Model for Multilingual Sentiment Analysis

Department of Computer Science and Engineering Dayananda Sagar College of Engineering Shavige Malleshwara Hills, Kumaraswamy Layout, Bengaluru 560078

Abstract The usefulness of understanding public opinion across linguistic boundaries cannot be overstated. Sentiment analysis is a great method for learning more about users' opinions and their opinions on social networks, such as movie review sites. Natural language processing (NLP) challenges jeopardize sentiment analysis' efficiency and accuracy. It has been demonstrated that hybrid models can utilize some of the advantages of classical models as long as deep learning models are capable of overcoming the obstacles connected with NLP. We want to look at this hybridization in this study.

Keywords- DeepLearning, Sentiment Analysis, LSTM, CNN

INTRODUCTION

The approach for multilingual sentiment analysis is based onemployingasentimentdictionarytotranslatewordsfor words in any native language. A text is analyzed in three stages: morphologically, using a sentiment dictionary to extract verbal sentiment from each word, and finally utilizingwordsentimentstoanalyzethetext.Ontweetsin English and Hindi, we did a sentiment categorization experiment. We used the assessment standards "Accuracy," "Precision," "Recall," and "F1 score" to evaluate our classifier's performance to that of different preceding classifiers. The experimental results suggest thatourclassifiermaybeutilizedforsentimentanalysisin multilingualism since its performance is unaffected by languagevariations.

Deep learning hybrid models have been suggested for analyzing social network data for sentiment analysis. We investigate the performance of combining SVM, CNN, and LSTM on eight datasets of tweets and reviews using two word embedding approaches, Word2vec and BERT. Followingthat,wecontrastedfourhybridmodelsthathad been created with single models that had been reviewed. These tests were carried out to explore if hybrid models couldbeappliedtoawiderangeofdatasettypesandsizes. The impacts of various datasets, feature extraction methods, and deep learning models on sentiment polarity

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analysis were investigated. The findings of our sentiment polarity analysis studies demonstrate that hybrid models beat all other models examined. Deep learning models paired with SVM give better sentiment analysis findings thanwhenusedalone.Thedependabilityofhybridmodels employingSVMisgreaterthanthatofmodelswithoutitin most of the datasets evaluated; however, the computational time for hybrid models using SVM is substantially longer. We also discovered that the algorithms' efficacy is heavily influenced by the datasets' features and quality. We recognise that the context of the datasethasasubstantialimpactonthesentimentanalysis algorithms we employ. We intend to examine the efficacy of hybrid approaches for sentiment analysis on hybrid datasets and multiple or hybrid settings in order to get a deeper knowledge of a particular topic, such as business, marketing, or medicine. The capacity to relate feelings to relevant context in order to give consumers specific individualized feedback and suggestions drives its adoption.

We providea multilingual sentimentanalysismethod that uses a sentiment dictionary to do word for word translation in any native language. The phases in this technique are as follows: text morphological analysis, sentiment dictionary based word sentiment extraction, andsentiment basedtextsentimentextraction.OnEnglish and Hindi tweets, we conduct a sentiment classification experiment. Using the assessment standards "Accuracy," "Precision," "Recall," and "F1 score," we compare the performance of our classifier in the experiment to the performance of several preceding classifiers. The experimental findings show that our classifier is suitable for sentiment analysis in multilingualism since its performanceisunaffectedbylanguagevariations.

Usingsocialnetworkdata,hybriddeeplearningmodelsfor sentiment analysis were built. The performance of integrating SVM, CNN, and LSTM with two word embedding methods, Word2vec and BERT, was assessed on eight textual datasets comprising tweets and reviews. Then we compared four hybrid models to single models thathadpreviouslybeenstudied.Theseresearcharebeing conducted to determine if hybrid models and hybrid

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techniques can adapt to a broad variety of dataset types and sizes. Using a range of datasets, feature extraction approaches, and deep learning models, we assessed the reliability of sentiment polarity analysis. Combining deep learning models with the SVM approach produces better results than using a single model when performing sentiment analysis. In terms of dependability, hybrid modelsthatuseSVMoutperformmodelsthatdonotinthe majority of datasets tested; nevertheless, the computational time for hybrid models that include SVM is much longer. Furthermore, wediscovered that the quality and quantity of the datasets had a significant influence on the performance of the algorithms. The context of the dataset has a significant impact on sentiment analysis methodologies.Weproposetoexaminetheusageofhybrid methodologies for sentiment analysis on hybrid datasets and numerous or hybrid contexts in order to gain a more comprehensive knowledge of a particular topic. Its usefulness arises from its capacity to relate attitudes to relevantfactsinordertodeliverClientsareprovided with individualized feedback and suggestions. Sentiment analysis is difficult to do without considering a range of semantic and syntactic limitations as well as the terminology of the input text. This research led to the creation of a sentiment analysis deep learning model that consists of one layer CNN architecture and two layers of LSTMs. Word embedding models can be used as the input layer in this approach. Research indicates that the proposed model can enhance accuracy by up to 11.6 percent and outperform existing techniques on many benchmarks. The proposed model makes use of CNN for featureextractionandLSTMforrecurrence.

MOTIVATION AND BACKGROUND

Sentiment analysis, which aims to extract subjective information from texts, is an important machine learning issue.Sentimentanalysisisintrinsicallytiedtotextmining and natural language processing. This helps you understand the overall polarity of the review or how the reviewer felt about a specific topic. Based on sentiment analysis, we might be able to determine whether the reviewer felt "glad," "sad," "angry," etc. when writing the review. In assessing the performance of a film, movie reviewsarecrucial.

Although numerical/star ratings provide a quantitative indication of a film's success or failure, film reviews provide a more qualitative analysis of the film. A textual movie review educates us about the film's good and bad points, and a more in depth evaluation of a film review may reveal if the picture meets the reviewer's overall expectations.Asa resultof the project,sentiment analysis will be availablein a range oflanguages, givingita strong

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analytical tool that is not limited by linguistic limitations. In comparison to standard models, the hybrid model surpassesthembycombiningaspectsfromothermodels.

RELATED WORK

[1] Thispaperdiscussesthecoreofdeeplearningmodels and associated approaches used in sentiment analysis for social network data. Before feeding input data to a deep learning model, the words embedding are employed. To perform sentiment analysis, we examined the architectures of DNN, CNN, and RNN based on word embedding . A number of experiments have been conducted to test DNN, CNN, and RNN models using datasetsrangingfromtweetstoreviews.Thereisa model comparison as well as some associated field studies. This data,togetherwiththeresultsofothermodel tests,paints acompletepictureoftheusageofdeeplearningmodelsfor sentiment analysis and their integration with text preparation.

[2] LSTM should be utilized to examine stock investor sentiments on stock swings, according to the paper. The author defines a model with seven separate phases for sentiment analysis. Participants are expected to perform tasks including data collection, data cleansing, manual sorting, feature extraction, LSTM model training, sentimentclassification,andsentimentanalysis.

The positive group's accuracy is 80.53 percent, while the negative group's accuracy is 72.66 percent, both of which aregreaterthantheaccuracyoftheChengandLin model, which used a sentiment dictionary to assess investor sentiment and stock return in the Chinese stock market. The paper expressly states the benefits of LSTM over a vanilla RNN network in sentiment analysis, and it is proposed that LSTM be utilized instead to get higher accuracyinanalysis.Becausethecomputingcostandtime required for the analysis are not factored into the model, the result of using LSTM for sentiment analysis is confusing. Following the examination, DNN, CNN, and hybrid approaches were discovered to be the most often utilized models for sentiment polarity analysis. The study alsodiscoveredthattypicaltechniqueslikeCNN,RNN,and LSTM were separately analyzed and judged to have the bestaccuracy.

[3] Based on our findings, we show that convolutional neural networks can outperform data mining in stock sentiment analysis. Documents are represented as bag of word vectors in the standard data mining approach for text categorization. These vectors represent the existence of words in a text but not their order in a sentence. In

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certaincircumstances,it'sclearthatwordorderinfluences the tone of a comment. You can utilise n grams to efficiently extract a document's sentiment and solve this problem with CNN. According to the investigation in this article examining and comparing a number of standard Deep Learning techniques, convolutional neural networks outperform logistic regression for sentiment analysis. Convolutional neural networks outperform other models intermsofaccuracy.Thestudyfailstodiscusstheuseofa combinationofmodelstoincreaseoverallperformanceby leveragingthecharacteristicsofeachmodel.

[4] AsabenchmarkforSentimentAnalysisonTwitter,the authors used data from the SemEval 2013 competition. Both terms and messages were classified in this project. Subtask b, which consisted of classifying a message into three categories: good, negative, and neutral, received the most attention (message level polarity classification). Usinglexicalandmachinelearningapproachestoevaluate Twitter sentiment, this study examines how negation handling impacts performance and how SWN based feature computation compliments it. To avoid misclassification by the SVM classifier, we created exceptionsincaseswhereanegationcueispresentbutno negation sense is present. The research asserts unequivocally that hybrid deep learning models are employed for improved analysis, and that utilizing hybrid modelsconsiderablyincreasesanalysisaccuracy.

[5] The author proposed a model that incorporated characteristics from different social context levels in the paper. This approach evolved from previous systems that only categorized users at the individual level. It also used communitydetectiontofindweaktiesbetweenuserswho were not directly connected in the network. The combinationisprojectedtooutperformatvariouslevelsof confidence in the labels in the context and with diverse degrees of social network sparsity. In a variety of situations,the proposedmethodhasbeen proved to work for both types of categorisation. Several datasets are examined in order to evaluate the model. The amount of datasets that could be included in the evaluation was limited due to the requirement for a social context. Because it comprised a more densely linked network of users,theRTMinddatasetappearedtobethemostsuited ofthethree.WhenCRANKwascomparedtootherbaseline modelsinthatdataset,thefindingsweremixed.Themodel was created by combining data from many levels of social settings, and it was not confined to user level categorization for community identification, and it also discovered weak relationships between users. There is no comparison between various community models and various graph traversal strategies, leaving the topic of

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model efficacy with other community techniques wide openandambiguous.

[6] Thisarticle'sauthorsdescribenumerousdeeplearning modelsandsuggestthathybridmodelsarethebestoption for sentiment analysis. Among the hybrid models, the authorshighlighttheCNN LSTMhybrid.

CNN can assist you in determining how to extract data characteristics. Long terminteractions,ontheotherhand, necessitate the inclusion of more convolution layers, and capturing dependencies gets more challenging as the duration of an input sequence in a neural network increases.Theconvolutional neural network layeris quite deep in practice. Associations between word sequences canberepresentedusingLSTMmodels.

A CNN LSTM hybrid model for sentiment analysis is presented in the study. The suggested hybrid CNN LSTM model outperformed CNN and LSTM models independently in two benchmark movie review datasets. By 91 percent, the suggested Hybrid CNN LSTM model outperformed traditional machine learning and deep learning models. The study examines a number of individual and hybrid sentiment analysis models, such as CNN LSTM, which are projected to be more accurate than theirrivals.

[7] Based on a one layer CNN architecture and two LSTM layers, the study developed an Arabic sentiment analysis deep learning model. Based on this architecture, FastText supports word embedding. The results of a multi domain dataset show that it is quite successful, with scores of 89.10 percent precision, 92.14 percent recall, 92.44 percent F1 Score, and 90.75 percent accuracy, respectively.Theeffectofwordembeddingmethodologies on Arabic sentiment categorization was comprehensively exploredinthiswork,revealingthattheFastTextmodelis a more acceptable choice for extracting semantic and syntactic information. The performance of the proposed modelisalsoevaluatedusingNBandKNNclassifiers.SVM is the top performing classifier, according to the statistics, with an accuracy improvement of up to 3.92 percent. Due to the effectiveness of CNNs in feature extraction and LSTM's recurrent nature, the proposed model outperformed state of the art approaches on a number of benchmarks with a +11.6 percent accuracy increase. The study specifies explicitly how accurate and effective the hybrid deep learning model is with layers and compact architecture. Multidomain datasets make the model more adaptableandaccurate.

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[8] The author's major focus in this study is on comprehendingthetext'smixedlinguisticcharacter,which includes both formal and informal textual material. Because user generated material frequently incorporates localized slangs as a means of expressing genuine emotions. The author employs a variety of methods to examine the data's subjectivity and polarity. This study considered not just formal languages, but also informal andlimited resourcelanguages.Theauthoralsosuggested using a hybrid architecture to create sentiment analysis resources.Thefinalmethodologiesappliedinthedifferent components,aswellasthequalityofresourcesaccessible, arecriticaltothecorrectnessofthesuggestedframework.

[9] Bing, Google, and Moses are three distinct machine translation(MT)systemsusedbythisauthor.Theauthors discovered that SMT systems can collect training data for languages other than English, and sentiment analysis systemscanperformsimilarlytoEnglish.Here,theauthor carried out a number of tests. In the first experiment, the data is translated from English to three other languages: German, Spanish, and French, and the model is trained separately for each language. In the second experiment, the author combined all of the translations of the training data obtained for the same language using the three different MT systems. SMO, AdaBoost M2, and Bagging classifiers are used by the author. However, combining all of the translated training data significantly raises the trainingdata'snoiselevel.

[10] The author tries to do sentiment analysis on languages with the greatest datasets, such as English, and then reuse the model for languages with low resources. The authors use RNN to build a sentiment analysis model basedonEnglishreviews.Therobusttechniqueofutilizing a single model trained on English reviews beats the baseline considerably in various languages. As a result, it can deal with a variety of languages. The author created a sentiment analysis model tailored to a particular domain justforthisarticle.

[11] The author of this article examines the feasibility of building sentiment detection and classification models in languages with fewer/no resources than English, emphasizing the importance of translation quality on sentiment classification performance, using machine translationandsupervisedmethods.Inthetrainingphase, the author could see that improper translations result in an increase in features, sparseness, and greater difficulty inrecognisingahyperplanethatseparatesthepositiveand negative instances. The extracted features are insufficiently informative for the classifier to learn when the translation quality is poor. The results of the testing canbeusedtoassessthequalityofthetranslation.Inthis

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case, poor translation quality causes a decline in performance.

[12] Theauthorintroducesauniquehybriddeeplearning model in this research that purposefully integrates multiplewordembeddingstrategies(Word2Vec,FastText, character level embedding) with various deep learning methods(LSTM,GRU,BiLSTM,CNN).Theproposedmodel incorporatesinformationfromseveraldeeplearningword embedding algorithms and classifies texts based on their emotionalcontent.Severaldeeplearningmodelsknownas basicmodelswerealsobuiltinordertoconductaseriesof experiments to evaluate the proposed model's performance. When comparing the suggested model's performance to that of previous research, it is clear that the new model outperforms the others. To test the suggested model's performance. The author carried out two separate trials. In the first experiment, twelve basic deeplearningmodelswerecreated.Therearefourtypesof deep learning. Twelve basic deep learning models were built in the first experiment. Four deep learning models (CNN, LSTM, BiLSTMGRU) were combined with three distincttextrepresentationmethods(Word2Vec,FastText, and character level embedding). FastText and Word2Vec embedding, both word representation approaches, performed better with RNNs models in the trial. The combinationofBiLSTMandFastTextinawordembedding technique yielded the highest classification accuracy of 80.44 percent. With a mix of CNN and character level representation, accuracy was 75.67 percent. With our suggested combination of CNN and BiLSTM with fastText and character embedding, we achieved an accuracy of 82.14percentclassificationsuccess.Asaconsequence,the suggested model's performance is superior to that of existingbasicmodels.Thesuggestedmodel'sperformance was compared to that of a prior research on the same dataset in the second experiment. The M Hybrid author obtained 82.14 percent, compared to 69.25 percent in earlierresearchonthedataset.Theauthorcategorizedthe dataset used in this work with various significant deep learning algorithms in order to confirm the correctness of our model. In light of the presented strategy, the author suggests combining several text representation methods forahigherclassificationaccuracyrate.

[13] CNN and LSTM are used in this study to build information channels for Vietnamese sentiment analysis. Thisscenario gives aninnovativeand efficient method for combiningthebenefitsofCNNwithLSTM.Theauthoralso assessedtheirmethodonthecorpusandtheVLSPcorpus. On the two datasets, the suggested model outperforms SVM, LSTM, and CNN, according to the experimental findings. The author also gathered 17,500 reviews from Vietnamese social media platforms and labeled them as

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good, neutral, or negative. This method combines the benefits of CNN and LSTM into a single model. In comparison to other models, which produces the best results?Thereisroomforimprovementinperformanceon unambiguousscenariosaswellascaseswithbothpositive andnegativeattitudes.

[14] The purpose of this research is to develop a semi automatedsentimentanalysislearningsystemthatcan be changed in response to language changes in order to remaincurrent.Thisisahybridstrategythatemploysboth lexicon basedandmachinelearningmethodstodetectthe polarityoftweets.Severaldatasetswerechosentoputthe suggested approach to the test. The accuracy for a 3 class classification challenge was 73.67 percent, while the accuracy for a 2 class classification problem was 83.73 percent. The semi automated learning component was proven to improve accuracy by 17.55 percent. The author offers a hybrid approach for Arabic Twitter sentiment analysis that gives high accuracy and dependable performanceinadynamicsettingwherelanguageanalysis systems must intelligently cope with these ongoing changes.

[15] Theauthorpresentsahybridsentimentclassification model that incorporates a Manhattan LSTM (MaLSTM) basedonarecurrentneuralnetwork(RNN),alsoknownas long short term memory (LSTM), and support vector machines (SVM). The suggested technique uses LSTM to learn the hidden representation before employing SVM to identifyattitudes.SVM basedrepresentationsoftheLSTM are used to determine attitudes from an IMDB movie review dataset based on the learned representations. In comparison to existing hashtag based models, the proposed model outperforms them. The proposed system (MaLSTM) shows that merging Siamese LSTM with SVM yieldsanoutstandingtextclassificationmodel.Whenthere are a lot of sentences, a pooling layer is created, and then the SVM is used to categorize them. Because it can detect hidden unit representations of sentences, the model may be employed in real time applications. According to the findings, the approach is competitive with state of the art algorithms on a variety of text classification tasks. Regularization strategies can improve classification in neuralnetworkmodelswithdropout.

[16] In this research, the author presents a novel hybrid deep learning architecture for sentiment analysis in resource constrained languages. Convolutional Neural Networks were also used to train sentiment embedding vectors(CNNs).Tochoosetheimprovedqualities,amulti objective optimization (MOO) framework is employed. Finally,thesentimentenhancedvectoracquiredisutilized

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to train the SVM for sentiment classification. An assessment of the suggested approach for coarse grained (sentence level) and fine grained (aspect level) sentiment analysis on four independent Hindi datasets. He also validated the proposed technique on two benchmark English datasets. According to a performance evaluation, Across all datasets, the suggested technique outperforms existing state of the art systems. When a sentence lacks a distinct sentiment identifier, the algorithm has a more difficulttimeproperlypredictingthesentiment.

[17] This study presents a hybrid learning system that incorporates both deep and shallow learning aspects. Not only can a hybrid technique classify single language text sentiment, but it can also classify bilingual text sentiment. Recurrent neural networks with long short term memory (RNNs with LSTM), Naive Bayes Support Vector Machine (NB SVM), word vectors, and bag of words are among the models investigated. Experiments demonstrate that accuracy may reach 89 percent, and that the hybrid technique outperforms any other method alone. The hybrid technique for multi language text sentiment classificationisanoveltyinthisworksinceitincludesboth generativeanddiscriminativemodels.

[18] The author suggests utilizing convolutional neural networks to classify sentiments. Experiments with three well known datasets show that employing consecutive convolutional layers for relatively long texts works well, and that our network beats state of the art deep learning models. The author showed in his research that using manyconvolutionallayersinarowincreasedperformance on reasonably long texts. The suggested CNN models obtained around 81 percent and 68 percent accuracy for binary and ternary classification, respectively. Despite the lengthy content, the convolutional layers helped to improve performance. The suggested CNN models had a ternary classification accuracy of 68 percent, which isn't extremelygood.

[19] The author of this study discusses and contrasts previous work in the subject of multilingual sentiment analysis. The goal is to see if the approaches allow for precise implementation and reliable replication of the claimed result, and whether the precision seen by the author is less than that defined in the original methodologypublications.Authorusedseveralwaysusing SVM classifierI, such as lexicon based, corpus based, hybrid, supervised learning, and so on, and compared the reportedaccuracywiththeaccuracythatauthorobtained. The paper offers insights into the authors' various methodologiesaswellashisresearch,whichpaintsaclear pictureoftheaccuraciesofvariousapproaches.

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[20] Theauthorofthisstudymixesmanymodelsintoone. We've observed that the hybrid method beats all other models,whethersingleormultiplelanguages,orboth,and to evaluate each model's contribution, we combine two models at a time to build the hybrid model. Time. Experiments have shown that each model works in a distinct way. Although different languages are handled differently, they all contribute to the overall image. This modelisamixofthetwo.Asthepenaltycoefficientdrops, over fitting becomes more prevalent and dangerous. The accuracy of the models is affected by many iterations. As the number of iterations grows, the hybrid model that matches the training data gets increasingly accurate. The accuracy of training data, on the other hand, significantly improves.Theprecisionoftestresultsisdeteriorating.

CONCLUSION

We conclude that using TF IDF rather than combining deeplearningwithwordembeddingisoptimal.Moreover, CNN provides a good balance between accuracy and processingtime,comparedtoothermodels.Thereliability ofRNNishigherthanthatofCNNonmostdatasets,butits computation time is much longer. At long last, a decision was reached. The study found that the algorithm's effectiveness is highly reliant on the input data. The datasets' characteristics allow testing deep learning algorithms with bigger datasets simpler. When compared to solo CNN and LSTM models in two benchmark movie review datasets, the suggested Hybrid CNN LSTM model performedsurprisinglywell.

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