[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117786-en":3,"doc-seo-117786-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117786,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons","This paper proposes two sentiment classification techniques for Gujarati text film reviews: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA). GLSA uses the GujSentiWordNet sentiment lexicon to generate features via sentiment scores, while GMLSA tests five machine-learning classifiers (LR, RF, KNN, SVM, and Naive Bayes with TF-IDF/count vectorizers). Five datasets validate accuracy and compare models using accuracy, precision, recall, and F-score. Results show ML-based methods improve average accuracy by 3–10% over lexicon-based classification.","Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons  \nParita Shah1,* Priya Swaminarayan2, Maitri Patel3  \n1Department of Computer Engineering, Sarva Vidyalaya Kelavani Mandal managed Vidush Somany Institute of Technology and Research, Kadi, India 2Faculty of Information Technology and Computer Science, Parul University,  \nVadodara, India  \n3Department of Computer Engineering, Gandhinagar University, India  \n*[Email: paritaponkiya@gmail.com](Email: paritaponkiya@gmail.com)  \nAbstract. In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learningbased and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.  \nKeywords: Gujarati text; lexicon; machine classifier; movie reviews; sentiment analysis.  \n1 Introduction  \nText categorization is a subset of opinion classification for the evaluation of people’s views and attitudes toward various subjects. Usage of social media platforms has increased in recent years, which has resulted in the generation of a huge amount of data on the web. Many different online platforms are available, such as retail, entertainment and content communities, messaging, and blogging services, where users can express their opinions. This results in large data that cannot be analyzed manually, necessitating the use of a method such as sentiment analysis, which offers atomization.  \nIndividual users’ perspectives differ from one another. Therefore, it is critical to evaluate many different opinions to offer more realistic thoughts about a topic and a large number of opinions must be analyzed. The bulk of the material on the  \nReceived December 13th, 2021, Revised July 26th, 2022, Accepted for publication September 6th, 2022. Copyright © 2023 Published by IRCS-ITB, ISSN: 2337-5787, DOI: 10.5614/itbj.ict.res.appl.2023.17 .1.1  \ninternet is in English, but owing to the increased awareness of online media, information in regional languages is also increasing quickly. So far, not much attention has been paid in the literature to Indian languages, particularly languages like Gujarati [1] .  \nAccording to our literature survey, lexicon-based and machine learning-based approaches have been utilized for sentiment classification. Machine learning (ML) algorithms are used to predict sentiment in ML-based methods [2] . On the other hand, sentiment lexicons are utilized in lexicon-based sentiment analysis. Lexicons are characterized as either lexicon-based or corpus-based, based on theresources used to determine sentiment polarity. Lexicon-based methods begin with seed emotion words and then proceed with synonyms and antonyms. Corpus-based methods start with sentiment seed words and then build a huge corpus of opinion words in the same context [3] .  \nDue to a lack of resources for Gujarati, the authors propose an algorithm that relies on a machine-learning classifier as well as a sentiment lexicon. Because there was no widely available dataset of movie reviews written in Gujarati, adataset of movie reviews in Gujarati was generated. Both suggested methods","cbCainL8LqDlQsZP","https://ap.wps.com/l/cbCainL8LqDlQsZP","pdf",523619,1,16,"English","en",105,"# Introduction\n## Related Work","[{\"question\":\"What are the two proposed approaches for sentiment classification in Gujarati film reviews?\",\"answer\":\"The paper proposes GLSA (Gujarati Lexicon Sentiment Analysis) and GMLSA (Gujarati Machine Learning Sentiment Analysis). GLSA relies on GujSentiWordNet, while GMLSA uses multiple classifiers with TF-IDF/count vector features.\"},{\"question\":\"Which sentiment resources and classifiers are used in the methods?\",\"answer\":\"GLSA uses the GujSentiWordNet lexicon and sentiment scores for feature generation. GMLSA evaluates logistic regression, random forest, k-nearest neighbors, support vector machine, and naive Bayes with TF-IDF and count vectorizer.\"},{\"question\":\"How are the experiments evaluated and what is the main performance outcome?\",\"answer\":\"Performance is measured using accuracy, precision, recall, and F-score across five datasets. The machine-learning approach achieves an average 3–10% higher accuracy than the lexicon-based approach.\"}]","Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons | PDF",1785679556,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"sentiment-classification-for-film-reviews-in-gujarati-text-using-machine-learning-and-sentiment-lexicons","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/sentiment-classification-for-film-reviews-in-gujarati-text-using-machine-learning-and-sentiment-lexicons/117786/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What are the two proposed approaches for sentiment classification in Gujarati film reviews?","Question",{"text":76,"@type":77},"The paper proposes GLSA (Gujarati Lexicon Sentiment Analysis) and GMLSA (Gujarati Machine Learning Sentiment Analysis). GLSA relies on GujSentiWordNet, while GMLSA uses multiple classifiers with TF-IDF/count vector features.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which sentiment resources and classifiers are used in the methods?",{"text":81,"@type":77},"GLSA uses the GujSentiWordNet lexicon and sentiment scores for feature generation. GMLSA evaluates logistic regression, random forest, k-nearest neighbors, support vector machine, and naive Bayes with TF-IDF and count vectorizer.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the experiments evaluated and what is the main performance outcome?",{"text":85,"@type":77},"Performance is measured using accuracy, precision, recall, and F-score across five datasets. The machine-learning approach achieves an average 3–10% higher accuracy than the lexicon-based approach.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":29,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]