A Survey on Sentiment Analysis

Routray, Preeti; Swain, Chinmaya Kumar; Mishra, Smita Praya

doi:10.5120/13280-0527

Cited by 28 publications

(15 citation statements)

References 33 publications

(15 reference statements)

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“…The Support vector machine (SVM) is understood that executes properly as sentiment analysis [24]. SVM typifies preference, confines and makes usage of the mechanisms for the assessment and examines records, which are attained within the index area [25].…”

Section: ) Support Vector Machinementioning

confidence: 99%

Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

et al. 2021

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The proliferation of user-generated content on social media has made opinion mining an arduous job. As a microblogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate stability of the proposed approach on two more datasets, one binary and other multi-class dataset and achieved robust results.

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Section: ) Support Vector Machinementioning

confidence: 99%

Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

et al. 2021

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“…Untuk memilih tempat wisata yang cocok biasanya para pengunjung akan menentukan Untuk mendapatkan kesimpulan dari sebuah opini ada beberapa yang harus dipersiapkan salah satunya yaitu dengan memilih metode klasifikasi (clasfier). Metode klasifikasi yang digunakan dalam penelitian ini yaitu Naïve Bayes, dimana metode Naïve Bayes merupakan sebuah pengklasfikasian probabilistik sederhana dengan teknik machine learning yang popular untuk klasifikasi teks (Routray, Swain, & Mishra, 2013).…”

Section: Pendahuluanunclassified

Industri 4.0: Pengukuran Tingkat Kesiapan Industri Tekstil Dengan Metode Singapore Smart Industry Readiness Index

Utomo¹,

Setiastuti²

2019

techno

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The era of technology is disrupted at this time, better known as the Industrial Revolution 4.0, already been applied to a various field of each country. Industry 4.0 include Internet of Thing (IoT), Artificial Intelligence (AI), human-machine interface, 3-D printing, and Advanced Robotics. Industry 4.0 is expected to increase productivity, business efficiency, and competitiveness. Indonesia’s Ministry of Industry has designed ‘Making Indonesia 4.0’ by preparing a roadmap and strategy to meet industry 4.0. There are 5 (five) prioritize manufacturing industrial sectors: Food and Beverages, Textile and Apparel, Electronics, Chemical, and Automotive. For studies conducted in the textile and apparel industry, as one of the priority industries. The Research study was conducted to determine the level of readiness of the textile manufacturing industry to meet industry 4.0 based on aspects of Technology, Processes, and Organizations. The method used is The Singapore Smart Industry Readiness Index. By knowing this level of readiness, it will help the industry to know the position of the current level of readiness and what needs are needed to reach the level of industry 4.0. By knowing the position, is able to know the strengths and weaknesses of technology from the operational technology used, which then knows the technological priorities that are of concern by management to increase industrial competitiveness towards industrial level 4.0.Based on the results of the mapping, related to the level of readiness of the textile industry of PT. Grand Textile based on technological aspects (1.56), process aspects (1.33) and organizational aspects (2.00) amounted to 1.63; position at level 1 which is categorized as New Comer.

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“…Algoritma Naïve Bayes dipilih dikarenakan algoritma ini sangat cocok untuk short data text. Kelebihan NBC adalah metode sederhana tetapi memiliki akurasi serta performansi yang tinggi dalam pengklasifikasian teks (Routray, et al, 2013).…”

Section: Pendahuluanunclassified

Analisis Sentiment pada Sosial Media Twitter Menggunakan Naїve Bayes Classifier dengan Feature Selection Particle Swarm Optimization dan Term Frequency

Cahyono

2017

JIUP

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Pengguna media sosial saat ini sangat besar; dimana setiap orang mengungkapkan pendapat; komentar; kritik dan lain-lain. Data tersebut memberikan informasi yang berharga untuk dapat membantu orang atau organisasi dalam pengambilan keputusan. Jumlah data yang sangat besar tidak mungkin bagi manusia untuk membaca dan menganalisis secara manual. Ansalisis Sentiment merupakan proses dalam menganalisis; memahami; dan mengklasifikasi pendapat; evaluasi; penilaian; sikap; dan emosi terhadap suatu entitas tertentu seperti produk; jasa; organisasi; individu; peristiwa; topik; guna mendapatkan informasi. Penelitian ini bertujuan untuk memisahkan tweets berbahasa Indonesia pada media sosial twitter kedalam kategori positif; negatif dan netral. Metode naїve bayes Classifier (NBC) dengan feature selection Particle Swarm Optimization (PSO) diterapkan pada dataset untuk mengurangi atribut yang kurang relevan pada saat proses klasifikasi. Hasil pengujian menunjukan bahwa algoritma Naïve Bayes Classifier dengan feature selection Particle Swarm Optimization (PSO) menggunakan parameter term frequency (TF) dengan akurasi 97;48%.

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A Survey on Sentiment Analysis

Cited by 28 publications

References 33 publications

Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

Industri 4.0: Pengukuran Tingkat Kesiapan Industri Tekstil Dengan Metode Singapore Smart Industry Readiness Index

Analisis Sentiment pada Sosial Media Twitter Menggunakan Naїve Bayes Classifier dengan Feature Selection Particle Swarm Optimization dan Term Frequency

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