[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124355-en":3,"doc-seo-124355-105":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},124355,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Sentiment Analysis of Movie Reviews using Word Embeddings and Machine Learning Techniques","This study performs sentiment analysis of movie reviews using word embeddings and machine learning techniques. Sentiment analysis is treated as opinion mining, transforming textual words and documents into computable vectors through embedding methods. The work compares TF-IDF and BERT representations for sentiment classification on the IMDb dataset, applying SVM, XGBoost, and LSTM models. Results indicate that TF-IDF combined with SVM achieves the highest accuracy, outperforming more complex pipelines involving BERT with LSTM, supporting efficient feature-classifier pairing.","LAUTECH Journal of Engineering and Technology 19 (3) 2025: 155-160  \n10.36108/laujet/5202.91.0341  \nSentiment Analysis of Movie Reviews using Word Embeddings and Machine Learning Techniques  \nIpadeola I. O., Ojo J. A., and Adebayo I. G.  \nDepartment of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, Ogbomoso.  \n\n| Article Info |  ABSTRACT  In this study, sentiment analysis of movie reviews was carried out using word embeddings and machine learning techniques. Sentiment analysis, as an opinion mining technique, involves using feature extraction methods to understand the opinions and emotions expressed in text—particularly in domains such as movie reviews, where public sentiment plays a strong role in shaping consumer decisions. For sentiment analysis to be effective, text must be converted into a form that a computer can process. This involves transforming words or documents into vectors using word embedding techniques. Common techniques include Bag of Words, TF-IDF, and Word2Vec. In this study, TF-IDF and Bidirectional Encoder Representations from Transformers (BERT) were selected to compare their effectiveness in analyzing sentiment in movie reviews. The research used the IMDb dataset, which is widely recognized and commonly used in text mining tasks. Various machine learning models were applied, including Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM). Results showed that the combination of TF-IDF and SVM produced the highest accuracy, outperforming more complex models such as BERT with LSTM. The findings suggest that simpler word embedding techniques, when paired with effective classifiers, can give strong performance in sentiment analysis. |\n| --- | --- |\n| Article history:\u003Cbr>Received: June 12, 2025\u003Cbr>Revised: July 30, 2025\u003Cbr>Accepted: Aug. 4, 2025 |  |\n| Keywords:\u003Cbr>Sentiment analysis, Word embedding, Machine learning\u003Cbr>Corresponding Author:\u003Cbr>[igadebayo@lautech.edu.n](igadebayo@lautech.edu.n)g |  |\n\nINTRODUCTION  \nThe widespread adoption of social media and digital platforms has profoundly transformed how individuals communicate, share, and evaluate information. Platforms such as Twitter, Facebook, and Reddit have become central to public discourse, allowing users to voice opinions on topics ranging from products and services to politics and entertainment. For businesses and media industries, this user-generated content (UGC) has emerged as a crucial resource for gauging public sentiment and informing decision-making processes (Gibson et al., 2025; Rathor et al., 2024; Xu, 2024; Yang, 2024) .  \nSentiment analysis, a subfield of natural language processing (NLP), focuses on the computational identification and classification of opinions, emotions, and attitudes expressed in text (Zhou and Liu, 2023) . It is extensively applied in customer  \nfeedback systems, product and service reviews, political sentiment tracking, and entertainment media. In the film industry, sentiment analysis of movie reviews can help stakeholders better understand audience reactions and predict commercial success (Nair et al., 2024; Asha et al., 2023; Nkhata et al., 2025) .  \nA key factor influencing the accuracy of sentiment classification models is the method used for textual feature representation. Traditional approaches, such as Term Frequency–Inverse Document Frequency (TF-IDF) provide a statistical measure of word relevance across documents but do not account for semantic or contextual meaning. Despite this limitation, TF-IDF remains popular due to its simplicity and compatibility with classical machine learning algorithms like Support Vector Machines (SVM) and XGBoost (González-Carvajal and Garrido-Merchán, 2020; Kumar and Bansal, 2023;  \nSubramaniyaswamy et al., 2024) . In recent years, the field of NLP has seen significant advancements with the development of contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT) . These models generate dy","cbCaid4dyy8wrsWA","https://ap.wps.com/l/cbCaid4dyy8wrsWA","pdf",659471,1,6,"English","en",105,"# Article Info\n## Abstract\n## Keywords\n# Introduction\n## Sentiment analysis in NLP\n## Text feature representation (TF-IDF and contextual embeddings)\n## Research aim and evaluation metrics\n# Materials and Methods\n## Dataset description","[{\"question\":\"What dataset is used for sentiment analysis in this study?\",\"answer\":\"The study uses the IMDb movie reviews dataset, a widely adopted benchmark for sentiment classification tasks.\"},{\"question\":\"Which embedding techniques are compared for movie review sentiment?\",\"answer\":\"TF-IDF and BERT (Bidirectional Encoder Representations from Transformers) are compared as word/text representation methods.\"},{\"question\":\"Which model combination achieved the highest accuracy?\",\"answer\":\"The combination of TF-IDF with SVM produced the highest accuracy, outperforming BERT with LSTM and other tested pairings.\"}]","Sentiment Analysis of Movie Reviews using Word Embeddings and Machine Learning Techniques | PDF",1785821801,15,{"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-analysis-of-movie-reviews-using-word-embeddings-and-machine-learning-techniques","",{"@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-analysis-of-movie-reviews-using-word-embeddings-and-machine-learning-techniques/124355/",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","2026-08-04",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 dataset is used for sentiment analysis in this study?","Question",{"text":76,"@type":77},"The study uses the IMDb movie reviews dataset, a widely adopted benchmark for sentiment classification tasks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which embedding techniques are compared for movie review sentiment?",{"text":81,"@type":77},"TF-IDF and BERT (Bidirectional Encoder Representations from Transformers) are compared as word/text representation methods.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model combination achieved the highest accuracy?",{"text":85,"@type":77},"The combination of TF-IDF with SVM produced the highest accuracy, outperforming BERT with LSTM and other tested pairings.","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},[94,98,102,106,111,115,120,123,128,131,135],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"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"]