Research on university email analysis based on SVM-RFE and Transformer-TBAM by LI Zhen, LI Zhichao, CHEN Lin

“…At present, deep learning methods are the main approach for text sentiment analysis, but existing methods have not fully utilized the features in Chinese text. …”
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Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM) by Primandani Arsi, Retno Waluyo

“…Application of sentiment analysis using machine learning methods shows that there are several methods that are often used. …”
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A ANALISIS SENTIMEN PARA KANDIDAT PILPRES 2024 DENGAN MODEL BAHASA BERT by LUIS RICARDO PANDIANGAN, IGN LANANG WIJAYAKUSUMA

“…This study aims to perform sentiment analysis better to understand people's perceptions towards each 2024 presidential candidate. …”
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Inescapable Media: The Role of Social Media Data Mining in Understanding American Adolescent Media Engagement by Peng Cheryl Siyuan

“…This study utilizes innovative data mining techniques such as sentiment analysis, trend detection, and network analysis; it allows insights into how social interactions impact psychological and emotional growth. …”
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State of Art for Semantic Analysis of Natural Language Processing by Dastan Hussen Maulud, Subhi R. M. Zeebaree, Karwan Jacksi, Mohammed A. Mohammed Sadeeq, Karzan Hussein Sharif

“…The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal. …”
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Machine Learning Applications based on SVM Classification A Review by Dakhaz Mustafa Abdullah, Adnan Mohsin Abdulazeez

“…Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. …”
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Real-Time Football Match Prediction Platform by An Zhongqi

“…Future work will focus on enhancing model algorithms and incorporating more complex data sources, such as social media sentiment analysis, to further improve prediction accuracy.…”
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PENERAPAN ARTIFICIAL INTELLIGENCE (AI) DALAM PERAMALAN AKUNTANSI TINJAUAN LITERATUR DAN AGENDA PENELITIAN MASA DEPAN by Gilang Surya Pratama, Agus Munandar

“…The analysis results show significant improvements in forecasting accuracy using AI technology, with Machine Learning achieving 78% accuracy in revenue forecasting, Deep Learning 85% in financial trend prediction, and Natural Language Processing 89% in sentiment analysis. Major implementation challenges include data quality, infrastructure limitations, and data security. …”
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Research on Sarcastic Emotion Recognition Based on Multiple Feature Fusion by Si Kaihao

“…Sarcasm detection significantly enhances the performance of various natural language processing applications, such as sentiment analysis, opinion mining, and stance detection. …”
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Let the Customers Speak Their Hearts Out: The Role of Verbosity and Emotions in Online Viewer-to-Viewer Engagement by Fahad Mansoor Pasha, Fatima Habib, Komal Kamran, Akbar Azam, Zeeshan Ali, Dildar Hussain

“…This study examines the combined impact of emotions, emojis, and verbosity on online viewer-to-viewer engagement, focusing on their interaction in shaping engagement behaviors. Using sentiment analysis with the Syuzhet package in R Studio and logistic regression on over 15,000 YouTube comments from the “YouTube Ads Leaderboard: 2021 Cannes Edition,” this research identifies key drivers of replies to initial comments. …”
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A frustratingly easy way of extracting political networks from text. by Naim Bro

“…This approach showcases the novel integration of GPT-4's capabilities in entity recognition, relation extraction, entity linking, and sentiment analysis into a single cohesive process. Based on a corpus of 1009 Chilean political news articles, the study validates the graph extraction method using 'legislative agreement', i.e., the proportion of times two politicians vote the same way. …”
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Comparison of the Performance of Pegasos and Traditional Models in the Task of Sentiment Classification of Product Reviews by Zhu Di

“…However, most of the existing research literature only uses traditional models or does not have a scientific comparison between models, and only provides training steps for a model that can be used for text sentiment analysis. Therefore, in this study, three models (two traditional models and one new model Pegasos) were selected for comparison. …”
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Analisis Sentimen untuk Evaluasi Reputasi Merek Motor XYZ Berkaitan dengan Isu Rangka Motor di Twitter Menggunakan Pendekatan Machine Learning by Ferdian Maulana Akbar, Robby Hermansyah, Sofian Lusa, Dana Indra Sensuse, Nadya Safitri, Damayanti Elisabeth

“…Note that this study does not include the deployment process of the machine learning model or dashboard creation, nor does it address brand reputation or sentiment analysis on other social media platforms such as TikTok or Instagram. …”
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Rekomendasi Pengembangan Fasilitas Wisata Tugu Pahlawan Surabaya Melalui Visualisasi Dashboard Hasil Klasifikasi Analisis Sentimen Ulasan Pengunjung by Fawwaz Roja Mahardika, Ahmad Afif Supianto, Nanang Yudi Setiawan, Raden Sandra Yuwana, Endang Suryawati

“…Sentiment analysis was carried out using the Support Vector Machine on 2180 review datas for the last 2 years taken from Google Reviews. …”
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Analisis Sentimen Berbasis Aspek Pada Ulasan Pelanggan Restoran Menggunakan Algoritma Support Vector Machine (Studi Kasus: Depot Bamara) by Muhammad Fariz Firdaus, Dian Eka Ratnawati, Nanang Yudi Setiawan

“…One solution that can be implemented is using aspect-based sentiment analysis with the help of machine learning. Aspect-based sentiment analysis provides more focused knowledge on each aspect of the restaurant, and the use of machine learning allows the utilization of reviews with minimal human resources. …”
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The changing language and sentiment of conversations about climate change in Reddit posts over sixteen years by Gabriele Fariello, Dariusz Jemielniak

“…Here, we analyze 16 years of Reddit discussions, encompassing 11.5 billion posts, to examine how language surrounding climate change has evolved over time from 2005 to 2021. We applied sentiment analysis, polarity, subjectivity, and readability metrics to discussions of “global warming” and “climate change”. …”
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Distrust Spillover in Sharing Accommodation: Evidence From Airbnb in Beijing by Xin Jin, Bo Wang, Ning Ma

“…Beijing Airbnb listings data were collected and analyzed using sentiment analysis, machine learning, and econometric statistics in English and Chinese languages. …”
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Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study by Natalia K Rojas, Sam Martin, Mario Cortina-Borja, Roz Shafran, Lana Fox-Smith, Terence Stephenson, Brian C F Ching, Anaïs d'Oelsnitz, Tom Norris, Yue Xu, Kelsey McOwat, Emma Dalrymple, Isobel Heyman, Tamsin Ford, Trudie Chalder, Ruth Simmons, Snehal M Pinto Pereira

“…A random subsample of 411 (5%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England. …”
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Biterm topic model of social network users’ sentiment by integrating word co-occurrence by Qiuyang GU, Bao WU, Chunhua JU

“…With the increasing number of social network users in recent years,text-based user sentiment analysis technology has been widely concerned and applied.However,data sparsity and low accuracy often reduce the accuracy and speed of emotion recognition methods.The user emotion Biterm topic model (US-BTM) was proposed which could find user preference and emotional tendency from the text of specific places,so as to effectively use Biterm for topic modeling.The strategy of user aggregation to form pseudo-documents was used,and word pairs were created for the whole corpus to solve the problems of data sparsity and short text.Then the topic was studied through the lexical co-occurrence model,so as to infer the topic with abundant corps-level information,and the purpose of accurately predicting the user’s interest,preference and emotion to the specific scene was achieved by analyzing the lexical matching set in the comment corpus under the specific scene and the emotion of the corresponding topic.The experimental results show that the method proposed can accurately capture users’ emotional tendency and correctly reveal users’ preference,which can be widely used in social network content description,recommendation,social network user interest description,semantic analysis and other fields.…”
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Viral Voices: Exploring Twitter as a Platform for Public Engagement in the 2022 Philippine Election by Christine B Tenorio, Yammie P. Daud, Lalevie C. Lubos

“…In particular, Social Network Analysis (SNA) was employed to better understand how Twitter was used in public conversation during the 2022 Philippine election,. In addition, Sentiment analysis was used to understand better the online users' positive, negative, or neutral responses. …”
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