Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter DOI Creative Commons

Rusydi Umar,

Sunardi Sunardi,

Muhammad Nur Ardhiansyah Nuriyah

и другие.

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Год журнала: 2023, Номер 7(4), С. 817 - 823

Опубликована: Авг. 12, 2023

On Twitter, users can post tweets, videos, and images. It can, however, also be disruptive difficult. To categorize the material improve searchability, hashtags are crucial. This study focuses on examining opinions of Twitter who participate in trending topics. The algorithms K-Nearest Neighbor (KNN) Support Vector Machine (SVM) used for sentiment analysis. data set comprises tweet information popular topics that was collected using API saved Excel format. SVM K-NN preparation, weighting, With 105 points, provides insight into user sentiment. identified 99% positive responses 1% negative with an accuracy 80%. KNN successfully 90% 10% responses, rate 71.4%. According to results, performs better when analyzing hashtag Twitter.

Язык: Английский

Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review DOI Creative Commons

Jamin Rahman Jim,

Md Apon Riaz Talukder,

Partha Malakar

и другие.

Natural Language Processing Journal, Год журнала: 2024, Номер 6, С. 100059 - 100059

Опубликована: Фев. 29, 2024

Sentiment analysis is a method within natural language processing that evaluates and identifies the emotional tone or mood conveyed in textual data. Scrutinizing words phrases categorizes them into positive, negative, neutral sentiments. The significance of sentiment lies its capacity to derive valuable insights from extensive data, empowering businesses grasp customer sentiments, make informed choices, enhance their offerings. For further advancement analysis, gaining deep understanding algorithms, applications, current performance, challenges imperative. Therefore, this survey, we began exploring vast array application domains for scrutinizing context existing research. We then delved prevalent pre-processing techniques, datasets, evaluation metrics comprehension. also explored Machine Learning, Deep Large Language Models Pre-trained models providing advantages drawbacks. Subsequently, precisely reviewed experimental results limitations recent state-of-the-art articles. Finally, discussed diverse encountered proposed future research directions mitigate these concerns. This review provides complete covering models, domains, challenges, directions.

Язык: Английский

Процитировано

55

A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine–Russia conflict DOI
Serpil Aslan

Applied Soft Computing, Год журнала: 2023, Номер 143, С. 110404 - 110404

Опубликована: Май 22, 2023

Язык: Английский

Процитировано

41

Enhanced ore classification through optimized CNN ensembles and feature fusion DOI
Mustafa Yurdakul, Kübra Uyar, Şakir Taşdemir

и другие.

Iran Journal of Computer Science, Год журнала: 2025, Номер unknown

Опубликована: Янв. 29, 2025

Язык: Английский

Процитировано

1

Sequence-wise multimodal biometric fingerprint and finger-vein recognition network (STMFPFV-Net) DOI Creative Commons
Sunusi Bala Abdullahi, Zakariyya Abdullahi Bature, Ponlawat Chophuk

и другие.

Intelligent Systems with Applications, Год журнала: 2023, Номер 19, С. 200256 - 200256

Опубликована: Июль 13, 2023

The existing multimodal biometric fingerprint and vein deep learning features were found effective for recognition. However, the current performance of was limited due to missing temporal image dependence extracted can obfuscate some important information because irrelevant features. This work proposes a sequence filtered spatial finger veins network (FS-STMFPFV-Net). overall proposed FS-STMFPFV-Net is achieved from two-channel independent improve variabilities. In first channel, generated by aligning images together inside generator. sequences are built into five layers convolution neural fusion model extract sequence-wise second channel where remembered in long short-term memory interactions between dimensions which finally generate their long-term dependencies as complementary information. These fused together, discriminative selected using feature selection. We have presented ReliefFS selection serve basis selecting compact CNN-based To evaluate FS-STMFPFV-Net, NUPT-FPV, FVC-2002-DBs, CASIA dataset used, provides fingerprint, finger-vein, palmprint databases, experimental validation. when evaluated standard protocols offers more than 97% accuracy across different databases ten times computationally friendly algorithms.

Язык: Английский

Процитировано

13

Sentiment Analysis of Using ChatGPT in Education DOI
Mohammad Tubishat, Feras Al‐Obeidat, Ahmed Shuhaiber

и другие.

Опубликована: Июль 25, 2023

This paper presents a study on the use of Chat Generative Pretrained Transformer (ChatGPT) in education. In this work, we propose sentiment analysis model tweets related to ChatGPT The purpose research is identify common sentiments, topics, and perspectives that are expressed towards education field based data collected from Twitter. Twitter was used collect 11830 about Topics emotions were extracted using NLP algorithms organized into distinct groups. Also, most frequent words positive negative opinion determined. findings indicate either or neutral, with small percentage expressing sentiments. addition, analyzes sentiments employment four different classifiers: Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF). According results, SVM classifier has highest accuracy 81.4 percent.

Язык: Английский

Процитировано

12

Strategic Business Insights through Enhanced Financial Sentiment Analysis: A Fine-Tuned Llama 2 Approach DOI

Pratiksha Agarwal,

Arun Kumar Gupta

2022 International Conference on Inventive Computation Technologies (ICICT), Год журнала: 2024, Номер unknown

Опубликована: Апрель 24, 2024

Sentiment analysis has emerged as a pivotal tool for distilling valuable insights from the vast expanse of textual information, significantly influencing financial markets. Traditional models, however, often fall short in navigating complex and nuanced terrain economic texts, struggling to decode industry-specific jargon rapid shifts sentiment inherent news. This discrepancy highlights notable gap applying general linguistic models domain-specific scenarios, presenting challenges leveraging market prediction strategic decision-making. To bridge this divide, refined approach is introduced, featuring an advanced adaptation Llama 2 7b-hf model. model specifically fine-tuned domain, employing parameter-efficient fine-tuning (PEFT) methods Simple Fine-tuning Trainer (SFTTrainer). Such modifications enhance model's attunement lexicon subtleties, ensuring precise sector's distinct characteristics while avoiding catastrophic forgetting. Demonstrated results signify considerable enhancements accuracy within sector. The achieves overall 89%, marking substantial improvements across negative, neutral, positive categories when juxtaposed with baseline counterparts. Accuracy elevates 37.3% conditions 84.4% post-initial adjustments, culminating at 89% after comprehensive fine-tuning, affirming enhanced proficiency decoding dynamics sentiment.

Язык: Английский

Процитировано

5

A BERT-encoded ensembled CNN model for suicide risk identification in social media posts DOI
Joy Gorai, Dilip Kumar Shaw

Neural Computing and Applications, Год журнала: 2024, Номер 36(18), С. 10955 - 10970

Опубликована: Март 28, 2024

Язык: Английский

Процитировано

4

Optimized neural attention mechanism for aspect-based sentiment analysis framework with optimal polarity-based weighted features DOI
Manevannan Ramasamy, M Elangovan

Knowledge and Information Systems, Год журнала: 2024, Номер 66(4), С. 2501 - 2535

Опубликована: Янв. 2, 2024

Язык: Английский

Процитировано

3

Sentiment evolution on social media: An in-depth study using Naive bayes for Twitter sentiment analysis DOI

Vandana Raturi,

Daksh Rawat,

H.K. Narang

и другие.

AIP conference proceedings, Год журнала: 2025, Номер 3224, С. 020022 - 020022

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

EmoNet: Deep Attentional Recurrent CNN for X (formerly Twitter) Emotion Classification. DOI Creative Commons
M. Jahangir Hossain, Md. Mithun Hossain, Md. Shakhawat Hossain

и другие.

IEEE Access, Год журнала: 2025, Номер 13, С. 37591 - 37610

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0