TFIDF based Feature Words Extraction and Topic Modeling for Short Text

Zhao, Guifen; Liu, Yanjun; Zhang, Wei; Wang, Yiou

doi:10.1145/3180374.3181354

Cited by 31 publications

(13 citation statements)

References 7 publications

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“…Zhao et al [34] proposed a system that can extract feature words, topics and reveal the most probable research area from research supported by NIH according to their titles. The authors of this paper have achieved this by using TF-IDF and LDA algorithms on the titles of 2000 NIH supported research in the 2017 fiscal year retrieved from the Research Portfolio Online Reporting Tools.…”

Section: Recommendations Based On Contentmentioning

confidence: 99%

Utilizing User Feedback Data for Content Recommendations

Shena¹

2022

Preprint

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Nowadays, Internet contains massive amount of information. In this environment, people who seek specific information could be overwhelmed by the options that they can reach through the Internet. To help users filter the information and overcome the information overload problem, recommender systems play an important role. Here, we deal with a specific recommendation problem – recommending content to users in a content management system utilizing users’ feedback data. We have tried both content-based and collaborative filtering approaches. In the content-based approach, once the content profile is built, user profile could be built based on different categories of user feedback data. We have explored the effect of these different feedback categories on the recommendation result. In the collaborative filtering approach, the feedback data is used for building the user-content rating matrix and matrix factorization is then applied. The experiment result shows that content-based approach outperforms collaborative filtering approach for this particular problem.

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Section: Recommendations Based On Contentmentioning

confidence: 99%

Utilizing User Feedback Data for Content Recommendations

Shena¹

2022

Preprint

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“…Term frequency-inverse document frequency (TF-IDF) and count-vectorizer properties were applied in this model. It's a statistical measure that indicates the significance of a word to a data or corpus collection document and TF-IDF features can extract words when needed [17]. We often get some errors during the data training and testing.…”

Section: Data Processingmentioning

confidence: 99%

Natural language processing based advanced method of unnecessary video detection

Moon

Salehin

Parvin

et al. 2021

IJECE

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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.</span>

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“…We have used MLP to calculate the implicit feedbacks. To find similarity scores between items, we have used two data mining techniques, such as: TF-IDF and LDA [5,6]. To include the item similarity scores into the MF model, we have defined and implemented an objective function based on an item-similarity based regularization term.…”

Section: Short Research Articlementioning

confidence: 99%

Recommending Curated Content Using Implicit Feedback

Roy

2020

AJRCoS

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Matrix factorization (MF) which is a Collaborative filtering (CF) based model, is widely used in the recommendation systems (RS). For our experiment, we collected data from a company's internal web site where curated contents are published and pushed to the employees. However, the size of the dataset is small and interaction data is also limited. We got a sparse matrix when we generated a user-item rating matrix. We have used Multi-Layer Perceptron (MLP) to calculate the rating scores from the implicit feedbacks. However, on this sparse dataset traditional content only or CF-only RSs do not work well. Here, we propose ahybrid RS that incorporates content similarity scores into an MLP-based MF-model. To integrate the content similarity scores into the MF, we have defined an objective function based on a regularization term. The experimental result shows that our proposed model demonstrates a better result than the traditional MF-based models.

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TFIDF based Feature Words Extraction and Topic Modeling for Short Text

Cited by 31 publications

References 7 publications

Utilizing User Feedback Data for Content Recommendations

Utilizing User Feedback Data for Content Recommendations

Natural language processing based advanced method of unnecessary video detection

Recommending Curated Content Using Implicit Feedback

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