Relevant bibliographies by topics / TF-IDF algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'TF-IDF algorithm'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "TF-IDF algorithm"

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Li, Jinye. "A comparative study of keyword extraction algorithms for English texts." Journal of Intelligent Systems 30, no. 1 (2021): 808–15. http://dx.doi.org/10.1515/jisys-2021-0040.

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Abstract This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF–IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF–IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF–IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice.
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R Wahyudi, M. Didik. "Evaluation of TF-IDF Algorithm Weighting Scheme in The Qur'an Translation Clustering with K-Means Algorithm." Journal of Information Technology and Computer Science 6, no. 2 (2021): 117–29. http://dx.doi.org/10.25126/jitecs.202162295.

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The Al-Quran translation index issued by the Ministry of Religion can be used in text mining to search for similar patterns of Al-Quran translation. This study performs sentence grouping using the K-Means Clustering algorithm and three weighting scheme models of the TF-IDF algorithm to get the best performance of the Tf-IDF algorithm. From the three models of the TF-IDF algorithm weighting scheme, the highest percentage results were obtained in the traditional TF-IDF weighting scheme, namely 62.16% with an average percentage of 36.12% and a standard deviation of 12.77%. The smallest results are shown in the TF-IDF 1 normalization weighting scheme, namely 48.65% with an average percentage of 25.65% and a standard deviation of 10.16%. The smallest standard deviation results in a normalized 2 TF-IDF weighting of 8.27% with an average percentage of 28.15% and the largest percentage weighting of 48.65% which is the same as the normalized TF-IDF 1 weighting.
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Xu, Dong Dong, and Shao Bo Wu. "An Improved TFIDF Algorithm in Text Classification." Applied Mechanics and Materials 651-653 (September 2014): 2258–61. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2258.

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Term frequency/inverse document frequency (TF-IDF) is widely used in text classification at present, which is borrowed from Information Retrieval. Based on this conventional classical TF-IDF formula, we present a new TF-IDF weight schemes named CTF-IDF. The experiment shows that the improved method is feasible and effective. Furthermore, from the subsequent evaluations using 10-fold cross-validation, we can see the CTF-IDF greatly improves the accuracy of text classification.
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Hidayah Mazlan, Nurul, and Isredza Rahmi A Hamid. "Evaluation of Feature Selection Algorithm for Android Malware Detection." International Journal of Engineering & Technology 7, no. 4.31 (2018): 311–15. http://dx.doi.org/10.14419/ijet.v7i4.31.23387.

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This paper synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. The Android features were filtered before detection process using TF-IDF algorithm. However, IDF is unaware to the training class labels and give incorrect weight value to some features. Therefore, the proposed approach modified the TF-IDF algorithm, where the algorithm focused on both sample and feature. Proposed algorithm applied considers the feature based on its level of importance. The related best features in the sample are selected using weight and priority ranking process. This increases the effect of important malware features selected in the Android application sample. These experiments are conducted on a sample collected from DREBIN dataset. The comparison between existing TF-IDF algorithm and modified TF-IDF (MTF-IDF) algorithm have been tested in various conditions such as different number of sample, different number of feature and combination of different types of feature. The analysis results show feature selection using MTF-IDF can improve malware detection analysis. MTF-IDF proved either using various kinds of feature or various kinds of dataset size, algorithm still effective for Android malware detection. MTF-IDF algorithm also proved that it could give appropriate scaling for all features in analyzing Android malware detection. Â
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Silalahi, Natalia, and Guidio Leonarde Ginting. "Rekomendasi Berita Berkaitan dengan Menerapkan Algoritma Text Mining dan TF-IDF." Bulletin of Computer Science Research 3, no. 4 (2023): 276–82. http://dx.doi.org/10.47065/bulletincsr.v3i4.266.

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News presentation is generally structured in such a way that the information presented is well grouped, but the use of electronic media does not necessarily offer complete news categories because not all of the space offered can be filled with good presentation, so special treatment is needed so that readers get the news. needed which is arranged based on recommendations. To arrange this research to be more structured, the authors carried out several stages in completing the research, namely the Problem Identification Stage, Literature Study Stage, Data Collection Stage, Text Mining and TF-IDF Algorithm Implementation Stage, and conclusions. The author implements the text mining and TF-IDF algorithms in processing news title data starting with the Text Mining Algorithm where this stage is a preprocessing stage with the aim that the data to be processed is a basic word so that the weighting process in the TF-IDF Algorithm is not too broad. After the text mining stage, it will proceed to the TF-IDF stage, namely weighting the terms in each document. Text mining and TF-IDF algorithms are able to provide appropriate news recommendations based on the highest similarity in meaning both in terms of topic and object of the news title, for future research it is recommended to use other algorithms such as cosine similar so that recommendations are not only generated from the suitability of words but can also see the similarity of meaning so that research results can be even better.
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Goswami, Puneet, and Vidya Kamath. "The DF-ICF Algorithm- Modified TF-IDF." International Journal of Computer Applications 93, no. 13 (2014): 28–30. http://dx.doi.org/10.5120/16276-6036.

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Saputra, Nova Adi, Khurotul Aeni та Nurul Mega Saraswati. "Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF". Scientific Journal of Informatics 11, № 1 (2024): 21–30. http://dx.doi.org/10.15294/sji.v11i1.48085.

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Purpose: Freedom in social media gives rise to the possibility of disturbing users through the sentences they send, which is limited by the Electronic Information and Transactions Law (UU ITE). This research aims to find an effective method for classifying hate speech text data, especially in Indonesian, with many categories expected to minimize this case.Methods: This study used 1.000 data from Twitter with five labels, including religion, race, physical, gender and other (invective or slander). The process started with several steps of preprocessing, data transformation using TF-IDF-ICSρF term weighting and data mining using an Improved KNN algorithm. Then, the results were compared with the TF-IDF and KNN methods to evaluate the differences.Result: Using TF-IDF-ICSρF and Improved KNN algorithms gets an average accuracy value of 88.11%, 17.81% higher compared with the same data and parameters to the K-Nearest Neighbor and TF-IDF algorithms, which get results of 70.30%.Novelty: Based on the comparison results, TF-IDF-ICSρF and Improved KNN methods can effectively classify hate speech sentences that have many labels with fairly good accuracy.
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Hashemzahde, Bahare, and Majid Abdolrazzagh-Nezhad. "Improving keyword extraction in multilingual texts." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5909. http://dx.doi.org/10.11591/ijece.v10i6.pp5909-5916.

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The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to that language holds a high rank based on the keywords criteria in other languages, as well. To achieve to this aim, the average TF-IDF of the candidate words were calculated for the same and the other languages. Then the words with the higher averages TF-IDF were chosen as the extracted keywords. The obtained results indicat that the algorithms’ accuracis of the multilingual texts in term frequency-inverse document frequency (TF-IDF) algorithm, graph-based algorithm, and the improved proposed algorithm are 80%, 60.65%, and 91.3%, respectively.
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Bahareh, Hashemzadeh, and Abdolrazzagh-Nezhad Majid. "Improving keyword extraction in multilingual texts." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5909–16. https://doi.org/10.11591/ijece.v10i6.pp5909-5916.

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The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to that language holds a high rank based on the keywords criteria in other languages, as well. To achieve to this aim, the average TF-IDF of the candidate words were calculated for the same and the other languages. Then the words with the higher averages TF-IDF were chosen as the extracted keywords. The obtained results indicat that the algorithms’ accuracis of the multilingual texts in term frequency-inverse document frequency (TF-IDF) algorithm, graph-based algorithm, and the improved proposed algorithm are 80, 60.65, and 91.3%, respectively.
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Chang, Hsien-Tsung, Shu-Wei Liu, and Nilamadhab Mishra. "A tracking and summarization system for online Chinese news topics." Aslib Journal of Information Management 67, no. 6 (2015): 687–99. http://dx.doi.org/10.1108/ajim-10-2014-0147.

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Purpose – The purpose of this paper is to design and implement new tracking and summarization algorithms for Chinese news content. Based on the proposed methods and algorithms, the authors extract the important sentences that are contained in topic stories and list those sentences according to timestamp order to ensure ease of understanding and to visualize multiple news stories on a single screen. Design/methodology/approach – This paper encompasses an investigational approach that implements a new Dynamic Centroid Summarization algorithm in addition to a Term Frequency (TF)-Density algorithm to empirically compute three target parameters, i.e., recall, precision, and F-measure. Findings – The proposed TF-Density algorithm is implemented and compared with the well-known algorithms Term Frequency-Inverse Word Frequency (TF-IWF) and Term Frequency-Inverse Document Frequency (TF-IDF). Three test data sets are configured from Chinese news web sites for use during the investigation, and two important findings are obtained that help the authors provide more precision and efficiency when recognizing the important words in the text. First, the authors evaluate three topic tracking algorithms, i.e., TF-Density, TF-IDF, and TF-IWF, with the said target parameters and find that the recall, precision, and F-measure of the proposed TF-Density algorithm is better than those of the TF-IWF and TF-IDF algorithms. In the context of the second finding, the authors implement a blind test approach to obtain the results of topic summarizations and find that the proposed Dynamic Centroid Summarization process can more accurately select topic sentences than the LexRank process. Research limitations/implications – The results show that the tracking and summarization algorithms for news topics can provide more precise and convenient results for users tracking the news. The analysis and implications are limited to Chinese news content from Chinese news web sites such as Apple Library, UDN, and well-known portals like Yahoo and Google. Originality/value – The research provides an empirical analysis of Chinese news content through the proposed TF-Density and Dynamic Centroid Summarization algorithms. It focusses on improving the means of summarizing a set of news stories to appear for browsing on a single screen and carries implications for innovative word measurements in practice.
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Dissertations / Theses on the topic "TF-IDF algorithm"

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Šabatka, Ondřej. "Reprezentace textu a její vliv na kategorizaci." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237263.

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The thesis deals with machine processing of textual data. In the theoretical part, issues related to natural language processing are described and different ways of pre-processing and representation of text are also introduced. The thesis also focuses on the usage of N-grams as features for document representation and describes some algorithms used for their extraction. The next part includes an outline of classification methods used. In the practical part, an application for pre-processing and creation of different textual data representations is suggested and implemented. Within the experiments made, the influence of these representations on accuracy of classification algorithms is analysed.
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Pettersson, Christoffer. "Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147.

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The goal of this project is to investigate any correlation between marketing emails and their receivers using machine learning and only a limited amount of initial data. The data consists of roughly 1200 emails and 98.000 receivers of these. Initially, the emails are grouped together based on their content using text clustering. They contain no information regarding prior labeling or categorization which creates a need for an unsupervised learning approach using solely the raw text based content as data. The project investigates state-of-the-art concepts like bag-of-words for calculating term importance and the gap statistic for determining an optimal number of clusters. The data is vectorized using term frequency - inverse document frequency to determine the importance of terms relative to the document and to all documents combined. An inherit problem of this approach is high dimensionality which is reduced using latent semantic analysis in conjunction with singular value decomposition. Once the resulting clusters have been obtained, the most frequently occurring terms for each cluster are analyzed and compared. Due to the absence of initial labeling an alternative approach is required to evaluate the clusters validity. To do this, the receivers of all emails in each cluster who actively opened an email is collected and investigated. Each receiver have different attributes regarding their purpose of using the service and some personal information. Once gathered and analyzed, conclusions could be drawn that it is possible to find distinguishable connections between the resulting email clusters and their receivers but to a limited extent. The receivers from the same cluster did show similar attributes as each other which were distinguishable from the receivers of other clusters. Hence, the resulting email clusters and their receivers are specific enough to distinguish themselves from each other but too general to handle more detailed information. With more data, this could become a useful tool for determining which users of a service should receive a particular email to increase the conversion rate and thereby reach out to more relevant people based on previous trends.<br>Målet med detta projekt att undersöka eventuella samband mellan marknadsföringsemail och dess mottagare med hjälp av oövervakad maskininlärning på en brgränsad mängd data. Datan består av ca 1200 email meddelanden med 98.000 mottagare. Initialt så gruperas alla meddelanden baserat på innehåll via text klustering. Meddelandena innehåller ingen information angående tidigare gruppering eller kategorisering vilket skapar ett behov för ett oövervakat tillvägagångssätt för inlärning där enbart det råa textbaserade meddelandet används som indata. Projektet undersöker moderna tekniker så som bag-of-words för att avgöra termers relevans och the gap statistic för att finna ett optimalt antal kluster. Datan vektoriseras med hjälp av term frequency - inverse document frequency för att avgöra relevansen av termer relativt dokumentet samt alla dokument kombinerat. Ett fundamentalt problem som uppstår via detta tillvägagångssätt är hög dimensionalitet, vilket reduceras med latent semantic analysis tillsammans med singular value decomposition. Då alla kluster har erhållits så analyseras de mest förekommande termerna i vardera kluster och jämförs. Eftersom en initial kategorisering av meddelandena saknas så krävs ett alternativt tillvägagångssätt för evaluering av klustrens validitet. För att göra detta så hämtas och analyseras alla mottagare för vardera kluster som öppnat något av dess meddelanden. Mottagarna har olika attribut angående deras syfte med att använda produkten samt personlig information. När de har hämtats och undersökts kan slutsatser dras kring hurvida samband kan hittas. Det finns ett klart samband mellan vardera kluster och dess mottagare, men till viss utsträckning. Mottagarna från samma kluster visade likartade attribut som var urskiljbara gentemot mottagare från andra kluster. Därav kan det sägas att de resulterande klustren samt dess mottagare är specifika nog att urskilja sig från varandra men för generella för att kunna handera mer detaljerad information. Med mer data kan detta bli ett användbart verktyg för att bestämma mottagare av specifika emailutskick för att på sikt kunna öka öppningsfrekvensen och därmed nå ut till mer relevanta mottagare baserat på tidigare resultat.
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Dočekal, Martin. "Porovnání klasifikačních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403211.

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This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Book chapters on the topic "TF-IDF algorithm"

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Li, Ji-Rui, Yan-Fang Mao, and Kai Yang. "Improvement and Application of TF * IDF Algorithm." In Information Computing and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25255-6_16.

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Jain, Rekha, Poonam Singh, and Shalini Puri. "Summarization of Daily News Using TextRank and TF-IDF Algorithm." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9043-6_26.

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Cheng, Long, Yang Yang, Kang Zhao, and Zhipeng Gao. "Research and Improvement of TF-IDF Algorithm Based on Information Theory." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14680-1_67.

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More, Priyanka Shivaprasad, Baljit Singh Saini, and Kamaljit Singh Bhatia. "Krill Herd (KH) algorithm for text document clustering using TF–IDF features." In Smart Computing. CRC Press, 2021. http://dx.doi.org/10.1201/9781003167488-60.

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Jiang, Lihui. "Automatic Evaluation of English Writing: Combining TF-IDF and Text Similarity Algorithm." In EAI/Springer Innovations in Communication and Computing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85225-1_4.

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Mishchenkо, Liudmyla, Iryna Klymenkо, and Valentyna Tkachenko. "The Fake News Recognition Method Based on Naïve Bayes with Improved TF-IDF Algorithm." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67348-1_12.

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Xu, Yuemei, Zuwei Fan, and Han Cao. "A Multi-task Text Classification Model Based on Label Embedding Learning." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9229-1_13.

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AbstractDifferent text classification tasks have specific task features and the performance of text classification algorithm is highly affected by these task-specific features. It is crucial for text classification algorithms to extract task-specific features and thus improve the performance of text classification in different text classification tasks. The existing text classification algorithms use the attention-based neural network models to capture contextualized semantic features while ignores the task-specific features. In this paper, a text classification algorithm based on label-improved attention mechanism is proposed by integrating both contextualized semantic and task-specific features. Through label embedding to learn both word vector and modified-TF-IDF matrix, the task-specific features can be extracted and then attention weights are assigned to different words according to the extracted features, so as to improve the effectiveness of the attention-based neural network models on text classification. Experiments are carried on three text classification task data sets to verify the performance of the proposed method, including a six-category question classification data set, a two-category user comment data set, and a five-category sentiment data set. Results show that the proposed method has an average increase of 3.02% and 5.85% in F1 value compared with the existing LSTMAtt and SelfAtt models.
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Zhang, Sen. "Restaurant Recommendation System Based on TF-IDF Vectorization: Integrating Content-Based and Collaborative Filtering Approaches." In Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023). Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-370-2_62.

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Yang, Wei, and Lian Liu. "Analysis of research progress of sodium-ion battery based on bibliometric method and TF-IDF algorithm." In Advances in Energy Materials and Environment Engineering. CRC Press, 2022. http://dx.doi.org/10.1201/9781003332664-58.

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Gonçalves de Pontes, Diego Roberto, and Sergio Donizetti Zorzo. "PPMark: An Architecture to Generate Privacy Labels Using TF-IDF Techniques and the Rabin Karp Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32467-8_89.

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Conference papers on the topic "TF-IDF algorithm"

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Chaurasia, Diwesh, Prof Vimala Devi K, and Mudit Bhatta. "Enhancing Text Summarization through Parallelization: A TF-IDF Algorithm Approach." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696641.

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Hu, Huicong, Jiatong Chen, and Huijing Hu. "Digital Trade Related Policy Text Classification and Quantification Based on TF-IDF Keyword Algorithm." In 2024 International Symposium on Intelligent Robotics and Systems (ISoIRS). IEEE, 2024. http://dx.doi.org/10.1109/isoirs63136.2024.00062.

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Karo, Ichwanul Muslim Karo, Adidtya Perdana, and Sri Dewi. "Automatic Text Review Summarization of Digital Library System Application using TextRank Algorithm and TF-IDF." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747952.

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Hao, Hong, Aihua Liu, and Xuemei Chen. "Quality Risk Assessment and Prediction Method of Chinese Herbal Medicines Based on TF-IDF Algorithm and Naïve Bayes Algorithm." In 2024 International Conference on Computational Linguistics and Natural Language Processing (CLNLP). IEEE, 2024. http://dx.doi.org/10.1109/clnlp64123.2024.00024.

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Nema, Neetesh Kumar, Vivek Shukla, Amit Pimpalkar, and S. R. Tandan. "Sentiment Analysis of Depression and Anxiety Social Media Tweets Using TF-IDF Weighting and Supervised Learning Algorithm." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687916.

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Cheng, Xiangzhao, Ting Li, Zhuo Zhang, et al. "Keyword Extraction and Word Cloud Generation for Electricity Work Order Text Based on Attention Mechanism and TF-IDF Algorithm." In 2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2024. https://doi.org/10.1109/icemce64157.2024.10862810.

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Durachman, Yusuf, Syopiansyah Jaya Putra, Herlino Nanang, and Husni Teja Sukmana. "Analysis Sentiment of Public Opinion on Social Media Using Naïve Bayes and TF-IDF Algorithms." In 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024. http://dx.doi.org/10.1109/iccit62134.2024.10701191.

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R, Sandhya B., Shusank Chaudhary, Basant Pandit, Khem Raj Seth, and Aditya Jha. "Unleashing the potential of Natural Language Processing for News Link Article Summarization: Comparing TF-IDF and Text Rank Algorithms." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939237.

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Zaware, Sarika, Deep Patadiya, Abhishek Gaikwad, Sanket Gulhane, and Akash Thakare. "Text Summarization using TF-IDF and Textrank algorithm." In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021. http://dx.doi.org/10.1109/icoei51242.2021.9453071.

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Zhang, Zhe, and Zhifeng Wu. "Improved TF-IDF Algorithm Combined with Multiple Factors." In 2021 3rd International Conference on Applied Machine Learning (ICAML). IEEE, 2021. http://dx.doi.org/10.1109/icaml54311.2021.00109.

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