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Academic literature on the topic 'TF-IDF Weights'

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Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "TF-IDF Weights"

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S., Sai Manasa Bala, and Kumari Santoshi. "Comprehensive Analysis of Variants of TF-IDF Applied on LDA and LSA Topic Modelling." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 531–35. https://doi.org/10.35940/ijeat.D7669.089620.

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Present generation is fully connected virtually through many sources of social media. In social media, opinions of people for any post, news or about any product through comments or emoticon designed to express the satisfactory note. Market standards improve on this basis. There are different online markets like Amazon, Flipkart, Myntra improve their businesses using these reviews passed. Analyzing large scale opinion or feedback of individual’s helps to identify hidden insights and work towards customer satisfaction. This paper proposes for applying different weighting scheme of TF-IDF (Term Frequency-Inverse Document Frequency) for topic modeling methods LSA and LDA to cluster the topics of discussion from large scale reviews related to booming online market ‘Amazon’. The main focus of the paper is to observe the changes in the topic modeling by applying different weighting schemes of TF-IDF. In this work topic-based models like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Allocation) applied to various weighting schemes of TF-IDF and observed the changes of weights leads to variation of term frequency of different topics with respect to its documents. Results also show that the variation of term weights results changes in topic modeling. Visualization results of topic modeling clusters with different TF-IDF weighting schemes are presented.
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Wu, Ho Chung, Robert Wing Pong Luk, Kam Fai Wong, and Kui Lam Kwok. "Interpreting TF-IDF term weights as making relevance decisions." ACM Transactions on Information Systems 26, no. 3 (2008): 1–37. http://dx.doi.org/10.1145/1361684.1361686.

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Bounabi, Mariem, Karim Elmoutaouakil, and Khalid Satori. "A new neutrosophic TF-IDF term weighting for text mining tasks: text classification use case." International Journal of Web Information Systems 17, no. 3 (2021): 229–49. http://dx.doi.org/10.1108/ijwis-11-2020-0067.

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Purpose This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency (NTF-IDF), is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages. The paper also propose a comparative study between the popular FTF-IDF and NTF-IDF and their impacts on different machine learning (ML) classifiers for document categorization goals. Design/methodology/approach After preprocessing textual data, the original Neutrosophic TF-IDF applies the neutrosophic inference system (NIS) to produce weights for terms representing a document. Using the local frequency TF, global frequency IDF and text N's length as NIS inputs, this study generate two neutrosophic weights for a given term. The first measure provides information on the relevance degree for a word, and the second one represents their ambiguity degree. Next, the Zhang combination function is applied to combine neutrosophic weights outputs and present the final term weight, inserted in the document's representative vector. To analyze the NTF-IDF impact on the classification phase, this study uses a set of ML algorithms. Findings Practicing the neutrosophic logic (NL) characteristics, the authors have been able to study the ambiguity of the terms and their degree of relevance to represent a document. NL's choice has proven its effectiveness in defining significant text vectorization weights, especially for text classification tasks. The experimentation part demonstrates that the new method positively impacts the categorization. Moreover, the adopted system's recognition rate is higher than 91%, an accuracy score not attained using the FTF-IDF. Also, using benchmarked data sets, in different text mining fields, and many ML classifiers, i.e. SVM and Feed-Forward Network, and applying the proposed term scores NTF-IDF improves the accuracy by 10%. Originality/value The novelty of this paper lies in two aspects. First, a new term weighting method, which uses the term frequencies as components to define the relevance and the ambiguity of term; second, the application of NL to infer weights is considered as an original model in this paper, which also aims to correct the shortcomings of the FTF-IDF which uses fuzzy logic and its drawbacks. The introduced technique was combined with different ML models to improve the accuracy and relevance of the obtained feature vectors to fed the classification mechanism.
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Mohammed, Mohannad T., and Omar Fitian Rashid. "Document retrieval using term term frequency inverse sentence frequency weighting scheme." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1478. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1478-1485.

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The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing documents and queries as vectors comprising average term term frequency inverse sentence frequency (TF-ISF) weights instead of representing them as vectors of term TF-IDF weight and two basic and effective similarity measures: Cosine and Jaccard were used. Using the MS MARCO dataset, this article analyzes and assesses the retrieval effectiveness of the TF-ISF weighting scheme. The result shows that the TF-ISF model with the Cosine similarity measure retrieves more relevant documents. The model was evaluated against the conventional TF-ISF technique and shows that it performs significantly better on MS MARCO data (Microsoft-curated data of Bing queries).
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Mohannad, T. Mohammed, and Fitian Rashid Omar. "Document retrieval using term frequency inverse sentence frequency weighting scheme." Document retrieval using term frequency inverse sentence frequency weighting scheme 31, no. 3 (2023): 1478–85. https://doi.org/10.11591/ijeecs.v31.i3.pp1478-1485.

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The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing documents and queries as vectors comprising average term term frequency inverse sentence frequency (TF-ISF) weights instead of representing them as vectors of term TF-IDF weight and two basic and effective similarity measures: Cosine and Jaccard were used. Using the MS MARCO dataset, this article analyzes and assesses the retrieval effectiveness of the TF-ISF weighting scheme. The result shows that the TF-ISF model with the Cosine similarity measure retrieves more relevant documents. The model was evaluated against the conventional TF-ISF technique and shows that it performs significantly better on MS MARCO data (Microsoft-curated data of Bing queries).
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Deo, Tula Kanta, Rajesh Keshavrao Deshmukh, and Gajendra Sharma. "Comparative Study among Term Frequency-Inverse Document Frequency and Count Vectorizer towards K Nearest Neighbor and Decision Tree Classifiers for Text Dataset." Nepal Journal of Multidisciplinary Research 7, no. 2 (2024): 1–11. http://dx.doi.org/10.3126/njmr.v7i2.68189.

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Background: Text classification techniques are increasingly important with the exponential growth of textual data on the internet. Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorizer(CV) are commonly used methods for feature extraction. TF-IDF assigning weights to terms based on their frequency. CV simply counts the occurrences of terms. The performance of CV as well as TF-IDF are evaluated and compared with KNN and DT classifiers across text datasets. Methodology: The investigation begins with preprocessing. The feature vectors are created using both TF-IDF and CV. Feature vectors are passed into the KNN and DT classifiers at in training stage. Experiments are executed the usage of Kaggle's public database Ukraine 10K tweets sentiment_analysis dataset and the Womens ecommerce clothing reviews dataset. Findings: The average of precision, recall, f1 score and accuracy of KNN with TF-IDF were 84.5%, 87%, 83%, 87% respectively and KNN with CV were 83.5%, 87%, 83.5%, 87% respectively. Similarly, average of precision, recall, f1 score and accuracy of DT with TF-IDF were 89%, 89%, 89%, 89% respectively and DT with CV were 89%, 89.5%, 89.5%, 89.5% respectively. The results obtained in this research is consistent with previous similar research result. Conclusions: The performance of TF-IDF is almost similar as CV for a particular dataset and a particular classifier in this study. Novelty: The experiment performed using these classifiers and feature extraction methods on the datasets is a novelty and contribution of this research.
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Muhammad Kiko Aulia Reiki, Yuliant Sibaroni, and Erwin Budi Setiawan. "Comparison of Term Weighting Methods in Sentiment Analysis of the New State Capital of Indonesia with the SVM Method." International Journal on Information and Communication Technology (IJoICT) 8, no. 2 (2023): 53–65. http://dx.doi.org/10.21108/ijoict.v8i2.681.

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The relocation of the State Capital to “Nusantara”, which was inaugurated with the enactment of UU No. 3 of 2022, is a significant project that has sparked polemics among Indonesian citizens. Many people expressed their opinions and thoughts regarding the relocation of the State Capital on Twitter. This tendency of public opinion needs to be identified with sentiment analysis. In sentiment analysis, term weighting is an essential component to obtain optimal accuracy. Various people are trying to modify the existing term weighting to increase the performance and accuracy of the model. One of them is icf-based or tf-bin.icf, which combines inverse category frequency (ICF) and relevance frequency (RF). This study compares the tf-idf, tf-rf, and tf-bin.icf term weighting with the SVM classification method on the new State Capital of Indonesia topic. The tf-idf weighting results are still the best compared to the tf-bin.icf and tf-rf term weights, with an accuracy score of 88.0% a 1,3% difference with tf-bin.icf term weighting.
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Kim, Hyun-Jin, Ji-Won Baek, and Kyungyong Chung. "Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score." Applied Sciences 10, no. 13 (2020): 4590. http://dx.doi.org/10.3390/app10134590.

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This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms.
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Sudrajat, Ari, Ratna Rizky Wulandari, and Elvathna Syafwan. "Indonesian Language Hoax News Classification Basedn on Naïve Bayes." Journal of Applied Intelligent System 7, no. 1 (2022): 70–79. http://dx.doi.org/10.33633/jais.v7i1.5985.

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Hoax news in Indonesia causes various problems, therefore it is necessary to classify whether a news is in the hoax category or is valid. Naive Bayes is an algorithm that can perform classification but has a weakness, namely the selection of attributes that can affect accuracy so that it needs to be optimized by giving weights to attributes using the TF-IDF method. Classification using Naive Bayes and using TF-IDF as attribute weighting on a dataset of 600 data resulted in 82% accuracy, 84% precision, and 89% recall. The suggestion put forward is that it is better to use a larger number of datasets in order to produce higher accuracy.
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Syarif, Iwan, Rengga Asmara, and Nur Ulima Rusmayani. "Klasifikasi Keluhan Masyarakat pada Sosial Media Twitter terhadap Pelayanan Toko Online di Indonesia menggunakan Metode Cosine TF-IDF." BINA INSANI ICT JOURNAL 7, no. 1 (2020): 33. http://dx.doi.org/10.51211/biict.v7i1.1334.

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Abstrak: Berkembangnya toko online dan transaksi online di Indonesia pada saat ini diiringidengan berbagai permasalahan seperti keluhan pada pelayanan yang membahas mengenaiaplikasi, ketanggapan dan pengiriman. Dengan adanya permasalahan tersebut, perhitunganserta penilaian keluhan yang sering didapatkan oleh masing-masing toko online sangatdiperlukan. Dengan memanfaatkan tweet masyarakat yang ditujukan kepada toko online, datatweet tersebut akan diklasifikasikan ke dalam kategori pelayanan yang telah ditentukan.Pengolahan data berupa tweet membutuhkan proses preprocessing yaitu proses untukmendapatkan keyword dari data tweet yang telah didapatkan, proses preprocessing memilikitahapan seperti tokenizing, filtering dan stemming. Keyword yang telah didapatkan diolah untukmendapatkan nilai hasil klasifikasi yang didapatkan. Proses klasifikasi kategori pelayanan padapenelitian ini menggunakan metode Cosine TF-IDF dimana metode tersebut membutuhkanbobot dan dokumen pada setiap kategori. Metode yang dikembangkan telah diaplikasikan padapenelitian ini menghasilkan prosentase proses klasifikasi kategori pelayanan menggunakanmetode Cosine TF-IDF sebesar 63.1%.
 Kata kunci: analisis sentimen, klasifikasi, rule based classifier, cosine similarity, TF-IDF
 Abstract: The development of online stores and online transactions in Indonesia at this time isaccompanied by various problems such as complaints on services that discuss applications,responsiveness and delivery. With these problems, the calculation and assessment ofcomplaints that are often obtained by each online store is very necessary. By utilizingcommunity tweets aimed at online stores, the tweet data will be classified into predeterminedservice categories. Data processing in the form of tweets requires a preprocessing process,namely the process of getting keywords from the data tweets that have been obtained, thepreprocessing process has stages such as tokenizing, filtering and stemming. The keywordsthat have been obtained are processed to obtain the classification results obtained. The servicecategory classification process in this study uses the Cosine TF-IDF method where the methodrequires weights and documents in each category. The method developed has been applied inthis study to produce a percentage of the service category classification process using theCosine TF-IDF method of 63.1%.
 Keywords: sentiment analysis, classification, rule based classifier, cosine similarity, TF-IDF
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Book chapters on the topic "TF-IDF Weights"

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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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Joshi, Prasad A., Varsha M. Pathak, and Manish R. Joshi. "Sentiment Analysis from Social Media Data in Code-Mixed Indian Languages Using Machine Learning Classifiers with TF-IDF and Weighted Word Features." In Data-Intensive Research. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9179-2_16.

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Prabhudesai, Arya. "Generation of Hindi Word Embeddings and Their Utilization in Ranking Documents Using Negative Sampling Architecture, t-SNE Visualization and TF-IDF Based Weighted Average of Vectors." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16681-6_28.

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Kavitha, D., Shyam Venkatraman, Karthik CR, and Navtej S. Nair. "Machine Learning-Based Sentiment Analysis of Twitter Using Logistic Regression." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3502-4.ch020.

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Twitter sentiment analysis is crucial for understanding public opinion in the digital age. This project employs logistic regression, a machine learning approach, to identify emotions in tweets from the Sentiment 140 dataset. Exploratory data analysis (EDA) identifies patterns in emotion distribution. Various machine learning algorithms, such as logistic regression, etc., are then used to classify tweets as good, negative, or neutral. Text preprocessing techniques prepare data, but TF-IDF weights words based on their significance. The challenges include capturing the complexities of human emotions while also keeping up with the ever-changing nature of Twitter data. Despite these limitations, data analysis and logistic regression provide important insights into public sentiment, assisting decision-making in a range of businesses. Looking ahead, the study emphasises the need for additional research to strengthen sentiment analysis methodologies. This includes addressing context-dependent emotions, adapting to diverse domains, and considering ethical issues such as partiality.
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Hao, Weiyang, Xiang Li, and Qi Zhou. "A Study on the Prediction of Illegal Fishing Crime in the Yangtze River Basin." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231197.

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In 2020, the state issued a ten-year ban on fishing in the Yangtze River basin, which has put forward more stringent requirements for public security officers to combat illegal fishing crimes. At present, there are problems such as low control efficiency, limited monitoring scope and no direction for arrests in combating illegal fishing activities in the Yangtze River basin. In this paper, we use police data as samples, extract the characteristics of criminal personnel using the TF-IDF algorithm, then use these characteristics as weights, use the combination of coefficient of variation plus hierarchical analysis algorithm to predict the key suspect, and finally use the trajectory carving model to carve the front, middle and back ends of the suspect’s crime execution to establish an early warning model of illegal fishing crimes in the Yangtze River basin. It provides investigative ideas for the detection of illegal fishing of aquatic products, curbs the spread of illegally caught aquatic products, improves regulatory measures and forms a normal and long-term mechanism for prevention and control and governance.
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Kury Fabrício S.P. and Cimino James J. "Identifying Repetitive Institutional Review Board Stipulations by Natural Language Processing and Network Analysis." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-564-7-579.

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The corrections (“stipulations”) to a proposed research study protocol produced by an institutional review board (IRB) can often be repetitive across many studies; however, there is no standard set of stipulations that could be used, for example, by researchers wishing to anticipate and correct problems in their research proposals prior to submitting to an IRB. The objective of the research was to computationally identify the most repetitive types of stipulations generated in the course of IRB deliberations. The text of each stipulation was normalized using the natural language processing techniques. An undirected weighted network was constructed in which each stipulation was represented by a node, and each link, if present, had weight corresponding to the TF-IDF Cosine Similarity of the stipulations. Network analysis software was then used to identify clusters in the network representing similar stipulations. The final results were correlated with additional data to produce further insights about the IRB workflow. From a corpus of 18,582 stipulations we identified 31 types of repetitive stipulations. Those types accounted for 3,870 stipulations (20.8% of the corpus) produced for 697 (88.7%) of all protocols in 392 (also 88.7%) of all the CNS IRB meetings with stipulations entered in our data source. A notable peroportion of the corrections produced by the IRB can be considered highly repetitive. Our shareable method relied on a minimal manual analysis and provides an intuitive exploration with theoretically unbounded granularity. Finer granularity allowed for the insight that is anticipated to prevent the need for identifying the IRB panel expertise or any human supervision.
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Conference papers on the topic "TF-IDF Weights"

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Kang, Jia, Junfeng Zhao, and Zhengxin Li. "LogTIW:A log anomaly detection model based on TF-IDF weighted semantic features." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651316.

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Chibb, Mayura, Priyanka Vashisht, Anvesha Katti, and Ashima Nrang. "Enhancing Movie Recommendations: A Content Based Approach Using TF-IDF Weighted Word2Vec and Cosine Similarity." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841087.

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Lichouri, Mohamed, Khaled Lounnas, Zahaf Nadjib, and Rabiai Ayoub. "dzNLP at NADI 2024 Shared Task: Multi-Classifier Ensemble with Weighted Voting and TF-IDF Features." In Proceedings of The Second Arabic Natural Language Processing Conference. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.arabicnlp-1.84.

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Na Wang, Pengyuan Wang, and Baowei Zhang. "An improved TF-IDF weights function based on information theory." In 2010 International Conference On Computer and Communication Technologies in Agriculture Engineering (CCTAE). IEEE, 2010. http://dx.doi.org/10.1109/cctae.2010.5544382.

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AAlAbdulsalam, Abdulrahman Khalifa. "SQU-CS @ NADI 2022: Dialectal Arabic Identification using One-vs-One Classification with TF-IDF Weights Computed on Character n-grams." In Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.wanlp-1.45.

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Agarwal, Lucky, Kartik Thakral, Gaurav Bhatt, and Ankush Mittal. "Authorship Clustering using TF-IDF weighted Word-Embeddings." In FIRE '19: Forum for Information Retrieval Evaluation. ACM, 2019. http://dx.doi.org/10.1145/3368567.3368572.

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Abedzadeh, Ali, Reza Ramezani, and Afsaneh Fatemi. "A Weighted TF-IDF-based Approach for Authorship Attribution." In 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE). IEEE, 2021. http://dx.doi.org/10.1109/iccke54056.2021.9721474.

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Md. "Blending Weighted TF-IDF & BERT for Improving Semantic Search." In 2022 2nd International Conference on Advanced Research in Computing (ICARC). IEEE, 2022. http://dx.doi.org/10.1109/icarc54489.2022.9753875.

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Kaur, Gurleen, Gurleen Singh, and Pulkit Dwivedi. "Enhancing Language Representation with Emotional Intelligence: A Weighted TF-IDF Approach." In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2023. http://dx.doi.org/10.1109/ic3i59117.2023.10397972.

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Buck, Christian, and Philipp Koehn. "Quick and Reliable Document Alignment via TF/IDF-weighted Cosine Distance." In Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-2365.

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