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Journal articles on the topic 'TfIdf vectorizer'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Setiawan, Assegaff, Rasywir Errissya, and Pratama Yovi. "Experimental of vectorizer and classifier for scrapped social media data." TELKOMNIKA 21, no. 04 (2023): 815–24. https://doi.org/10.12928/telkomnika.v21i4.24180.

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In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided lear

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Rahmatul Kholiq, Muhammad Hatta, Wiranto Wiranto, and Sari Widya Sihwi. "News classification using light gradient boosted machine algorithm." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 206. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp206-213.

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News classification is a complex issue as people are easily convinced of misleading information and lack control over the spread of fake news. However, we ca n break the problem of spreading fake news with artificial intelligence (AI), which has developed rapidly. This study proposes a news classification model using a light gradient boosted machine (LightGBM) algorithm. The model is analyzed using two feature extraction techniques, count vectorizer and Tfidf vectorize r and compared with a deep learning model using long - short term memory (LSTM). The experimental evaluation showed that all L

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Kholiq, Muhammad Hatta Rahmatul, Wiranto Wiranto, and Sari Widya Sihwi. "News classification using light gradient boosted machine algorithm." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 206–13. https://doi.org/10.11591/ijeecs.v27.i1.pp206-213.

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News classification is a complex issue as people are easily convinced of misleading information and lack control over the spread of fake news. However, we can break the problem of spreading fake news with artificial intelligence (AI), which has developed rapidly. This study proposes a news classification model using a light gradient boosted machine (LightGBM) algorithm. The model is analyzed using two feature extraction techniques, count vectorizer and Tfidf vectorizer and compared with a deep learning model using long-short term memory (LSTM). The experimental evaluation showed that all Light

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4

Ramalingam, Gomathi, Logeswari S, M. D. Kumar, Manjula Prabakaran, Neerav Nishant, and Syed A. Ahmed. "Machine learning classifiers to predict the quality of semantic web queries." Scientific Temper 15, no. 01 (2024): 1777–83. http://dx.doi.org/10.58414/scientifictemper.2024.15.1.28.

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In this research, a classification framework to automatically identify well and poorly designed SPARQL queries is proposed. Evaluating SPARQL queries becomes a difficult challenging issue because of the query design and the volume of data to be handled. The proposed context applies various machine learning algorithms including decision trees, k nearest neighbours, support vector machine, and naive Bayes. In addition, two different feature extraction techniques called TFIDF measure and count vectorizer are measured to identify the key features. The experimental results show that the four machin

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Borkar, Sumedh. "Identifying Fake News Using Real Time Analytics." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 994–1000. http://dx.doi.org/10.22214/ijraset.2022.45406.

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Abstract: Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news

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Awan, Mazhar Javed, Awais Yasin, Haitham Nobanee, et al. "Fake News Data Exploration and Analytics." Electronics 10, no. 19 (2021): 2326. http://dx.doi.org/10.3390/electronics10192326.

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Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through m

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7

Bhuvaneshwari, K., Dr S. A. Jyothi Rani, and Dr V. V. Haragopal. "Sentiment Analysis of Tweets on Telangana State Government Flagship Schemes." International Journal of Engineering and Advanced Technology 12, no. 1 (2022): 23–27. http://dx.doi.org/10.35940/ijeat.a3794.1012122.

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Over the last decade, the usage of social media has evolved to a greater extent. Today, social media platforms like Twitter, facebook, snapchat are vastly used to incept the opinions of public about a particular entity. Social media has become a great source of text data. Text analytics plays a crucial role on social media data to give answers to a wide variety of questions about public feedback on many issues or topics. The primary objective of this work is to analyse the public opinion or sentiment in social media on Telangana state government welfare schemes. The purpose of sentiment analys

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Reddy, Vookanti Anurag, CH Vamsidhar Reddy, and Dr R. Lakshminarayanan. "Fake News Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 227–30. http://dx.doi.org/10.22214/ijraset.2022.41124.

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Abstract: This Project comes up with the applications of NLP (Natural Language Processing) techniques for detecting the 'fake news', that is, misleading news stories that comes from the non-reputable sources. Only by building a model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix, (word tallies relative to how often they’re used in other articles in your dataset) can only get you so far. But these models do not consider the important qualities like word ordering and context. It is very possible that two articles that are similar i

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9

Nandini, Mrs Kagita, Sreya Deepthi Puppala, Vadupu Varshita, Sudulagunta Ratna Megda, and Thommandru Sumanth. "Deep Hybrid System for Personalized Movie Recommendations." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 739–47. https://doi.org/10.36893/jnao.2025.v16i01.087.

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A proposal framework is a sort of programming application or calculation intended to recommend things, for example, items, films, melodies, articles to clients in light of their inclinations, ways of behaving, or comparable client's exercises. This paper presents an original crossover suggestion framework that coordinates content-based and cooperative separating approaches utilizing profound learning procedures to improve film proposals. Our model merges the metadata of movies, including genres, cast, and crew from the Movie Lens dataset with user ratings to construct a comprehensive feature s

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Terisri, Paladugula, Nandyala Hiranmayee, V. V. S. S. C. Ekantha S, Dungala Puthin, Kishor Ambati Karteek, and Tanmai Ramisetti Jyothi. "Sentimental Analysis using NLP." Sentimental Analysis using NLP 8, no. 12 (2023): 5. https://doi.org/10.5281/zenodo.10401483.

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Sentiment analysis is a subset of text analysis techniques that uses automatic text polarity detection. One of the main responsibilities of NLP (Natural Language Processing) is sentiment analysis, often known as opinion mining. In recent years, sentiment analysis has gained a lot of popularity. It is meant for people to build a system that can recognize and categorize sentiment or opinion as it is expressed in an electronic text. Nowadays, people who wish to purchase consumer goods prefer to read user reviews and participate in public online forums where others discuss the product. This is bec

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D.Swetha, D.Swetha. "Detecting Faux Information Using Machine Learning." International Journal of Scientific Development and Research 7, no. 9 (2022): 954–57. https://doi.org/10.5281/zenodo.10442975.

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Fake news is false or deceiving information presented as news. Fake news, or fake news websites, have no base in fact, but are presented as being factually accurate. Fake news has also been called junk news, pseudo-news, indispensable data, false news, humbug news and bullshit. Recent political events have led to an increase in the fashionability and spread of fake news. As demonstrated by the wide goods of the large onset of fake news, humans are inconsistent if not outright poor sensors of fake news. With this, been made to automate the process of fake news discovery. The most popular of sim

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12

Itoo, Rayees Ahmad. "Classifying Opinions and Sentiments on Social Networking Sites using Machine Learning Classifiers." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1613–23. http://dx.doi.org/10.22214/ijraset.2024.58664.

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Abstract: People now publish evaluations on social media for any product, movie, or location they visit as a result of the Web's rapid development. Customers and product owners can both benefit from the reviews posted on social media in order to assess their offerings. Compared to unstructured data, structured data is simpler to analyze. The reviews are mostly available in an unstructured format. Aspect-Based Sentiment Analysis extracts from the reviews the features of a product and then calculates sentiment for each feature. Sentiment analysis, also known as opinion mining, is a natural langu

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13

K., Bhuvaneshwari, S. A. Jyothi Rani Dr., and V. V. Haragopal Dr. "Sentiment Analysis of Tweets on Telangana State Government Flagship Schemes." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 1 (2022): 23–27. https://doi.org/10.35940/ijeat.A3794.1012122.

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<strong>Abstract: </strong>Over the last decade, the usage of social media has evolved to a greater extent. Today, social media platforms like Twitter, facebook, snapchat are vastly used to incept the opinions of public about a particular entity. Social media has become a great source of text data. Text analytics plays a crucial role on social media data to give answers to a wide variety of questions about public feedback on many issues or topics. The primary objective of this work is to analyse the public opinion or sentiment in social media on Telangana state government welfare schemes. The

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14

Shinde, Anjali, Essa Q. Shahra, Shadi Basurra, Faisal Saeed, Abdulrahman A. AlSewari, and Waheb A. Jabbar. "SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning." Sensors 24, no. 18 (2024): 6084. http://dx.doi.org/10.3390/s24186084.

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The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that remain underexplored in existing research. To address this, we merge a UCI spam dataset of regular text messages with real-world spam data, leveraging OCR technology for comprehensive analysis. The study employs a combination of traditional machine learning model

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15

Jiang, Xuehui. "A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines." Scientific Programming 2022 (April 1, 2022): 1–9. http://dx.doi.org/10.1155/2022/3330196.

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With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through

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16

Arya, Vishakha, Amit Kumar Mishra Mishra, and Alfonso González-Briones. "Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 1 (2022): 45–63. http://dx.doi.org/10.14201/adcaij.27348.

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The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire t

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17

"Identification of Duplication in Questions Posed on Knowledge Sharing Platform Quora using Machine Learning Techniques." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (2019): 2444–51. http://dx.doi.org/10.35940/ijitee.l3017.1081219.

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Quora, an online question-answering platform has a lot of duplicate questions i.e. questions that convey the same meaning. Since it is open to all users, anyone can pose a question any number of times this increases the count of duplicate questions. This paper uses a dataset comprising of question pairs (taken from the Quora website) in different columns with an indication of whether the pair of questions are duplicates or not. Traditional comparison methods like Sequence matcher perform a letter by letter comparison without understanding the contextual information, hence they give lower accur

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18

Venkatramulu, S., Md Sharfuddin Waseem, Arshiya Taneem, Sri Yashaswini Thoutam, Snigdha Apuri, and Nachiketh Nachiketh. "Research on SQL Injection Attacks using Word Embedding Techniques and Machine Learning." Journal of Sensors, IoT & Health Sciences, March 31, 2024. http://dx.doi.org/10.69996/jsihs.2024005.

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Most of the damage done by web application attacks comes from SQL injection attacks, in which the attacker(s) can change, remove, and read data from the database servers. All three tenets of security— confidentiality, integrity, and availability—are vulnerable to a SQL injection attack. Database management systems receive their queries in the form of SQL (structured query language). It is not a new field of study, but it is still important to detect and prevent SQL injection attacks. A method of SQL injection detection based on machine learning is proposed. Feature extraction, followed by impl

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19

Fatima, Rubab, Mian Muhammad Sadiq Fareed, Saleem Ullah, Gulnaz Ahmad, and Saqib Mahmood. "An Optimized Approach for Detection and Classification of Spam Email’s Using Ensemble Methods." Wireless Personal Communications, November 13, 2024. http://dx.doi.org/10.1007/s11277-024-11628-9.

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AbstractSince the advent of email services, spam emails have been a major concern because users’ security depends on the classification of emails as ham or spam. It’s a malware attack that has been used for spear phishing, whaling, clone phishing, website forgery, and other harmful activities. However, various ensemble Machine Learning (ML) algorithms used for the detection and filtering of spam emails have been less explored. In this research, we offer a ML-based optimized algorithm for detecting spam emails that have been enhanced using Hyper-parameter tuning approaches. The proposed approac

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20

Sahu, Laxminarayan, and Bhavana Narain. "FAKE NEWS DETECTION USING MACHINE LEARNING MULTI-MODEL METHOD." ShodhKosh: Journal of Visual and Performing Arts 5, no. 2 (2024). http://dx.doi.org/10.29121/shodhkosh.v5.i2.2024.1811.

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A fake news article that originates from an WhatsApp source is known as fake news. Fake news is becoming more and more prevalent on social media and other platforms, and this is a serious worry since it has the potential to have devastating effects on society and the country. This is why there has already been a lot of research done on its detection. This study uses supervised machine learning techniques to develop a product model through research and implementation of false news detection system. To put it briefly, this work will use a Naive Bayes classifier to build a model that can identify

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-, Kavitha I., Arshad Ahamed M. -, Deral Akshan A. -, Gokul S. -, and Kogul M. -. "Discerning Truth: Leveraging Naïve Bayes for Fake News Detection." International Journal For Multidisciplinary Research 6, no. 2 (2024). http://dx.doi.org/10.36948/ijfmr.2024.v06i02.18312.

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These days individuals get to know all the news, temperate and political undertakings through social media. The most deliberate is to redirect the truthfulness and inventiveness of the news. This kind of news spreading poses a serious threat to social cohesiveness and well-being since it fosters polarization in politics and mistrust among people. False news producers use elaborate, colorful traps to further the success of their manifestations, one of which is to incite the providers' emotions. The information-savvy community has responded by adopting measures to address the issue. Hence by uti

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"Interrogation of Sentiment Perusing with Hash Counting Vectorizer and Term Inverse Frequency Transformer using Machine Learning Classification." International Journal of Recent Technology and Engineering 8, no. 4 (2019): 3895–901. http://dx.doi.org/10.35940/ijrte.d8303.118419.

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With the fast growing technology, the business is moving towards increasing their profit by interpreting the customer satisfaction. The customer satisfaction can be analyzed in many ways. It is the responsibility of the business to analyze the customer satisfaction in order to improve their turnover and profit. With the current trend, the customers are giving their feedback through mobile and internet. With this overview, this paper attempts to analyze the sentiment of the customer feedback for the movie. The sentiment Analysis on movie Review dataset from the KAGGLE Machine learning repositor

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