Development of argument based opinion mining model with sentimental data analysis from twitter content

This article presents a methodology for using data from social networks, specifically from Twitter, to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets, and the processing of such information to run the diagnosis. To demonstrate the viability of this methodology, 358 Twitter leak reports were collected and analyzed in Mexico City from 1 May to 31 December 2022. From these reports, leak density and probability were calculated, which are metrics that can be used to develop forecasting algorithms, identify root causes, and program repairs. The calculated metrics were compared with those calculated through telephone reports provided by SACMEX, the entity that manages water in Mexico City. Results show that metrics obtained from Twitter and phone reports were highly comparable, indicating the usefulness and reliability of social media data for diagnosing leaks.

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“…Social [20] This article presents an argument-based opinion-mining model with sentimental data analysis for extracting specific arguments.…”

Section: Referencementioning

confidence: 99%

Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks

2023

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“…Analisis jejaring sosial menyoroti peran media sosial dalam menyebarkan sudut pandang dan memfasilitasi interaksi [7]. Kerangka kerja penambangan opini telah dirancang untuk menilai opini publik di Twitter, mencakup evaluasi sentimen dan pemanfaatan kata kunci dalam tweet [8].…”

Section: Pendahuluanunclassified

Kinerja Auto Labelling Pada Analisis Sentimen Terhadap Pasangan Calon Presiden 2024 Di Media Sosial X

Iriananda,

Putra,

Farhan

2023

ciastech

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Di era pasca Covid-19 pemanfaatan platform X telah meningkat pesat dan memainkan peran penting sebagai sumber opini publik. Media sosial memungkinkan individu untuk menyatakan pendapat mereka tentang peristiwa terkini. Respons positif terhadap demokrasi dan kebebasan berekspresi telah terlihat dalam penggunaan platform ini. Banyak individu menggunakan Twitter untuk menyampaikan sudut pandang dan komentar, Termasuk dalam konteks politik khususnya pada masa menjelang Pemilihan Presiden dan Wakil Presiden Indonesia 2024. Penelitian ini menekankan tantangan dalam menganalisis sentimen dalam tweet berbahasa Indonesia dan menyoroti penggunaan algoritma pembelajaran mesin untuk analisis sentimen. Penelitian ini menggunakan teknik web scraping untuk mengumpulkan data dari platform media sosial dan menerapkan metode preprocessing untuk membersihkan dan mengatur data. Ekstraksi fitur dilakukan menggunakan metode TF-IDF dan kombinasi Unigram, Bigram, dan Trigram (UniBiTri). Pendekatan Auto Labelling digunakan dengan algoritma VADER dan TextBlob, diikuti oleh klasifikasi SVM dengan mempertimbangkan rasio data latih dan data uji serta pengujian dengan berbagai kernel. Evaluasi dilakukan melalui confusion matrix dan laporan klasifikasi untuk menilai kinerja model. Temuan menunjukkan bahwa kinerja Auto Labelling TextBlob melampaui VADER, mencapai akurasi maksimum 99,33%, Precision 99,35%, dan recall 99,33%.

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“…advertisement, marketing) and other forms of expressions via virtual networks [5], [6], [7], [8], [9], [10]. Social networking sites such as Facebook, Twitter, WhatsApp, Instagram have become a very popular source to easily express their views with the help of text posts, status, stories, images, videos, and audios [11].…”

Section: Introductionmentioning

confidence: 99%

Improving Sentiment Analysis in Social Media by Handling Lengthened Words

et al. 2023

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Machines are continually being channelized in the current era of automation to deliver accurate interpretations of what people communicate on social media. The human species is today engulfed in the concept of what and how people believe, and the decisions made as a result are mostly dependent on the sway of the masses on social media platforms. The usage of internet as well as social media is booming day by day. Today, this ocean of data can be used for the fruitful purposes. Analysis of social media sentiment textual posts can supply knowledge and information that can be used in citizen opinion polling, business intelligence, social contexts, and Internet of Things (IOT)-mood triggered devices. In this manuscript, the main focus is the sentiment analysis based on Emotional Recognition (ER). The proposed system highlights the process of gaining actual sentiment or mood of a person. The key idea to this system is posed by the fact that if smile and laughter can be two different categories of being happy, then why not happpyyyyyy and happy. A novel lexicon based system is proposed that considers the lengthened word as it is, instead of being omitted or normalized. The aggregated intensified senti-scores of lengthened words are calculated using framed lexicon rules. After that, these senti-scores of lengthened words are used to calculate the level of sentiment of the person. The dataset used in this paper is the informal chats happened among different friend groups over Facebook, Tweets and personal chat. The performance of proposed system is compared with traditional systems that ignore lengthened words and proposed system outperform tradition systems by achieving 81% to 96% F-measure rate for all datasets.

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Development of argument based opinion mining model with sentimental data analysis from twitter content

Cited by 5 publications

References 35 publications

Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks

Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks

Kinerja Auto Labelling Pada Analisis Sentimen Terhadap Pasangan Calon Presiden 2024 Di Media Sosial X

Improving Sentiment Analysis in Social Media by Handling Lengthened Words

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