Kernelized eigenspace based fuzzy C-means for sensing trending topics on Twitter

Prakoso, Yudho; Murfi, Hendri; Wibowo, Arie

doi:10.1145/3239283.3239297

Cited by 5 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, "Film Reviews" becomes "film review." Case-folding helps ensure uniformity and consistency in the text data [23]. Tokenization, the process of splitting text or sentences into smaller units, called tokens [1].…”

Section: Data Preprocessingmentioning

confidence: 99%

Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine

Sutriawan,

Muljono,

Khairunnisa

et al. 2024

MMEP

View full text Add to dashboard Cite

The use of online movie streaming media has increased significantly, particularly among movie enthusiasts. However, fan comments are frequently informal and comprise informal language, subjectivity, and contexts that reflect their preferences. A significant challenge in sentiment analysis of movie reviews is how to classify sentiments in reviews that are often unstructured and subjective. This study aims to improve the accuracy of sentiment classification in movie reviews by proposing several methods, including a hybrid TF-IDF+N-Gram model that can extract pertinent information from word and phrase sequences in reviews. Then, feature selection with Information Gain (IG) is performed to identify the most informative sentiment classification features. This strategy seeks to overcome informal language and noise to improve review context comprehension. The results demonstrated a significant gain in the accuracy of sentiment classification. TFIDF+Bigram+IG achieved 78% accuracy (up 8% from 70% previously), and TFIDF+Trigram+IG achieved 66% accuracy (up 22% from 44% previously). Using this hybrid model, the study significantly enhanced the accuracy of sentiment classification, thereby enhancing the performance of SVM in the face of complex movie evaluations.

show abstract

Section: Data Preprocessingmentioning

confidence: 99%

Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine

Sutriawan,

Muljono,

Khairunnisa

et al. 2024

MMEP

View full text Add to dashboard Cite

show abstract

“…To avoid randomness, Alatas et al [23] proposed nonnegative double singular value decomposition (NNDSVD) as the initialization method of FCM in topic modeling. Prakoso et al [24] used eigenspacebased FCM (EFCM) for conducting the clustering process in low dimensional textual data. Fearing that dimension reduction would reduce accuracy, the authors used a kernel trick to improve accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Eligüzel

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

The focus of this study is to use social media to investigate the Russia-Ukraine conflict. With the assent of the Russian parliament, Russian President Vladimir Putin proclaimed that they will begin invading Ukraine on February 24, 2022. During the Russia-Ukraine conflict, social media, particularly Twitter, has been heavily used. For that reason, it becomes to strong tool for handling processes during the conflict such as political decision making, organizing humanitarian activities, and proving assistance for victims. As a result, social media becomes the most up-to-date, comprehensive, and large information source for current scenario analysis. A total of 65412 tweets are gathered as a dataset for analysis in the proposed study between February 24 and April 5. Then, for each tweet, a topic modeling method called Latent Dirichlet Allocation (LDA) is used to collect significant topics and their probabilities considering each tweets. Then, using the specified probabilities, Fuzzy c-means is utilized to generate clusters for the entire document. Finally, seven unique clusters have been gathered for processing. N-grams and network analysis are used to examine each resulting cluster for a better understanding. As a result of this study, worldwide public opinion, current situation of civilians, course of the conflict, humanitarian issues during the Russia-Ukraine conflict are extracted.

show abstract

“…Muliawati and Murfi (2017) designed a topic model using Eigen space-based Fuzzy C-Means (EFCM) clustering in which Singular Value Decomposition (SVD) is used for reduction in data dimension. Prakoso et al (2018) extended this work by introducing Kernel Eigen space-based Fuzzy C-Means (KEFCM) for sensing detecting trending topics. Trupthi et al (2018) utilized Probabilistic fuzzy C-means topic modelling for analysing user sentiments.…”

Section: Unsupervised Modelsmentioning

confidence: 99%

“…Table 11 shows the details of these datasets. (Korshunova et al 2019;Belford et al 2016;Kumar and Vardhan 2019;Ni et al 2018;Yan et al 2012;Fang et al 2017;Fang et al 2014;Muliawati and Murfi 2017;Prakoso et al 2018;Pornwattanavichai et al 2020;Capdevila et al 2017;Mustakim et al 2019;Zhang and Zhang 2020;Ali and Balakrishnan 2021;Abdulwahab et al 2022;Yu et al 2017;Indra and Pulungan 2019;Curiskis et al 2020;He et al 2020b;Fang et al 2017;Karami et al 2018Wandabwa et al 2021…”

Section: Web Snippets Datasetmentioning

confidence: 99%

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

et al. 2022

View full text Add to dashboard Cite

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly embraced by individuals, groups, and organizations as a valuable source of information. This social media generated information comes in the form of tweets or posts, and normally characterized as short text, huge, sparse, and low density. Since many real-world applications need semantic interpretation of such short texts, research in Short Text Topic Modeling (STTM) has recently gained a lot of interest to reveal unique and cohesive latent topics. This article examines the current state of the art in STTM algorithms. It presents a comprehensive survey and taxonomy of STTM algorithms for short text topic modelling. The article also includes a qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques. Moreover, a comparative analysis of the topic quality and performance of representative STTM models is presented. The performance evaluation is conducted on two real-world Twitter datasets: the Real-World Pandemic Twitter (RW-Pand-Twitter) dataset and Real-world Cyberbullying Twitter (RW-CB-Twitter) dataset in terms of several metrics such as topic coherence, purity, NMI, and accuracy. Finally, the open challenges and future research directions in this promising field are discussed to highlight the trends of research in STTM. The work presented in this paper is useful for researchers interested in learning state-of-the-art short text topic modelling and researchers focusing on developing new algorithms for short text topic modelling.

show abstract

Kernelized eigenspace based fuzzy C-means for sensing trending topics on Twitter

Cited by 5 publications

References 9 publications

Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine

Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

Contact Info

Product

Resources

About