Relevant bibliographies by topics / Tweet Classifier

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Tweet Classifier'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Tweet Classifier"

1

V, Ashwin. "Twitter Tweet Classifier." IAES International Journal of Artificial Intelligence (IJ-AI) 5, no. 1 (March 1, 2016): 41. http://dx.doi.org/10.11591/ijai.v5.i1.pp41-44.

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<p>This paper addresses the task of building a classifier that would categorise tweets in Twitter. Microblogging nowadays has become a tool of communication for Internet users. They share opinion on different aspects of life. As the popularity of the microblogging sites increases the closer we get to the era of Information Explosion.Twitter is the second most used microblogging site which handles more than 500 million tweets tweeted everyday which translates to mind boggling 5,700 tweets per second. Despite the humongous usage of twitter there isn’t any specific classifier for these tweets that are tweeted on this site. This research attempts to segregate tweets and classify them to categories like Sports, News, Entertainment, Technology, Music, TV, Meme, etc. Naïve Bayes, a machine learning algorithm is used for building a classifier which classifies the tweets when trained with the twitter corpus. With this kind of classifier the user may simply skim the tweets without going through the tedious work of skimming the newsfeed.</p>
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Harsehanto, Ireicca Agustiorini, and M. Didik R. Wahyudi. "Analysis of Personality Characteristic Using the Naïve Bayess Classifier Algorithm (Case Study Official Twitter of Basuki Tjahaja Purnama's and Anies Baswedan)." IJID (International Journal on Informatics for Development) 7, no. 2 (January 7, 2019): 14. http://dx.doi.org/10.14421/ijid.2018.07203.

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Abstract - This research uses data from social media Twitter based on the results of tweets from user_timeline @basuki_btp and @aniesbaswedan. This study uses 2100 tweet data. Data that has been collected is then pre-processed first and labeled manually. The next process is classification using the Naïve Bayess Classifier Algorithm using the Big Five Personality Theory. Based on the test results using 500 tweet data as training data and 1600 tweet data as testing data. The classification results obtained by using the Naïve Bayes Classifier Method and grouped in the "Big Five" personality groups: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism on tweet data in Indonesian.
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Tarigan, Thomas Edison, Robby C. Buwono, and Sri Redjeki. "Extraction Opinion of Social Media in Higher Education Using Sentiment Analysis." bit-Tech 2, no. 1 (October 30, 2019): 11–19. http://dx.doi.org/10.32877/bt.v2i1.92.

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The purpose of this research is to extract social media Twitter opinion on a tertiary institution using sentiment analysis. The results of sentiment analysis will provide input to universities as a form of evaluation of management performance in managing institutions. Sentiment analysis generated using the Naïve Bayes Classifier method which is classified into 4 classes: positive, normal, negative and unknown. This study uses 1000 data tweets used for training data needs. The data is classified manually to determine the sentiment of the tweet. Then 20 tweet data is used for testing. The results of this study produce a system that can classify sentiments automatically with 75% test results for sentiment, some obstacles in processing real-time tweets such as duplicate tweets (spam tweets), Indonesian structures that are quite complex and diverse.
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Talpur, Bandeh Ali, and Declan O’Sullivan. "Multi-Class Imbalance in Text Classification: A Feature Engineering Approach to Detect Cyberbullying in Twitter." Informatics 7, no. 4 (November 15, 2020): 52. http://dx.doi.org/10.3390/informatics7040052.

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Twitter enables millions of active users to send and read concise messages on the internet every day. Yet some people use Twitter to propagate violent and threatening messages resulting in cyberbullying. Previous research has focused on whether cyberbullying behavior exists or not in a tweet (binary classification). In this research, we developed a model for detecting the severity of cyberbullying in a tweet. The developed model is a feature-based model that uses features from the content of a tweet, to develop a machine learning classifier for classifying the tweets as non-cyberbullied, and low, medium, or high-level cyberbullied tweets. In this study, we introduced pointwise semantic orientation as a new input feature along with utilizing predicted features (gender, age, and personality type) and Twitter API features. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa (84%), classifier accuracy (93%), and F-measure (92%) metric. Overall, 40% of the classifiers increased performance in comparison with baseline approaches. Our analysis shows that features with the highest odd ratio: for detecting low-level severity include: age group between 19–22 years and users with <1 year of Twitter account activation; for medium-level severity: neuroticism, age group between 23–29 years, and being a Twitter user between one to two years; and for high-level severity: neuroticism and extraversion, and the number of times tweet has been favorited by other users. We believe that this research using a multi-class classification approach provides a step forward in identifying severity at different levels (low, medium, high) when the content of a tweet is classified as cyberbullied. Lastly, the current study only focused on the Twitter platform; other social network platforms can be investigated using the same approach to detect cyberbullying severity patterns.
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da Silva, Nádia F. F., Eduardo R. Hruschka, and Estevam R. Hruschka. "Tweet sentiment analysis with classifier ensembles." Decision Support Systems 66 (October 2014): 170–79. http://dx.doi.org/10.1016/j.dss.2014.07.003.

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Zuhri, Khoirul, and Nurul Adha Oktarini Saputri. "Analisis Sentimen Masyarakat Terhadap Pilpres 2019 Berdasarkan Opini Dari Twitter Menggunakan Metode Naive Bayes Classifier." Journal of Computer and Information Systems Ampera 1, no. 3 (September 17, 2020): 185–99. http://dx.doi.org/10.51519/journalcisa.v1i3.45.

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Twitter is a social media that is currently popular, where the public is free to comment and write anything. It is not uncommon for the public to comment with harsh words and even hate speech. The 2019 presidential election drew many comments, some praised, criticized and insulted. To be able to dig up information and classify a text, sentiment analysis is needed. In this study, sentiment analysis is a process of classifying textual documents into two classes, namely negative and positive sentiment classes. Opinion data were obtained from the Twitter social network in the form of tweets. The data used was 3337 tweets consisting of 80% training data and 20% training data. Training data is data with known sentiment. This study aims to determine whether a tweet is a positive or negative tweet conveyed on Twitter in Indonesian. The classification of tweet data uses the naïve Bayes classifier algorithm. The classification results of the test data show that the Naïve Bayes Classifier algorithm provides an accuracy value of 71%. The accuracy value for each sentiment is 71% for positive sentiment and 70% for negative sentiment
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Hafidz, Noor, and Dewi Yanti Liliana. "Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (April 28, 2021): 213–19. http://dx.doi.org/10.29207/resti.v5i2.2960.

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On March 2020 World Health Organization (WHO) has declared Covid-19 as global pandemic. As special agency of United Nation who responsible for international public healthy, WHO has done various actions to reduce this pandemic spreading rate. However, the handling of Covid-19 by WHO is not free from a number of controversies that gave rise to criticism and public opinion on the Twitter platform. In this research, a machine learning based classifier model has been made to determine the opinion or sentiment of the tweet. The dataset used is a set of tweets containing the phrase WHO and Covid-19 in period of March 1st until May 6th 2020 consisting of 4000 tweets with positive sentiments and 4000 tweets with negative sentiments. The proposed classifier model combined Support Vector Machine (SVM), N-Gram and Particle Swarm Optimization (PSO). The classifier model performance is evaluated using the value of Accuracy, Precision, Recall, and Area Under ROC Curve (AUC). Based on experiments conducted, the combination of SVM, N-gram (bigram), and PSO produced a pretty good performance in classifying tweet sentiment with values of Accuracy 0,755, Precision 0,719, Recall 0,837, and AUC 0,844.
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Silitonga, Wiranto Horsen, and Jay Idoan Sihotang. "Analisis Sentimen Pemilihan Presiden Indonesia Tahun 2019 Di Twitter Berdasarkan Geolocation Menggunakan Metode Naïve Bayesian Classification." TeIKa 9, no. 02 (October 31, 2019): 115–27. http://dx.doi.org/10.36342/teika.v9i02.2199.

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Pemilihan Presiden Indonesia 2019 ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Twitter. Semua orang bebas berpendapat tentang pasangan calon Presiden Indonesia 2019 tersebut. Sehingga memunculkan banyak opini, tidak hanya opini yang positif atau netral, ada pula opini negatif. Media sosial khususnya Twitter sekarang ini menjadi salah satu tempat promosi atau kampanye yang efektif dan efisien untuk menggait para pendukung. Dalam hal ini peneliti akan melakukan riset terhadap tokoh publik yang mencalonkan diri menjadi Presiden Indonesia. Metode penelitian yang digunakan dalam riset kali ini adalah algoritma klasifikasi Naïve Bayesian Classifer. Data yang digunakan adalah tweet berbahasa Indonesia dengan kata kunci Jokowi (#Jokowi2Periode) dan Prabowo (#PrabowoSandi) sebanyak 1009 data tweet selama 5 bulan dimulai dari 1 September 2019 sampai 31 Januar1 2019. Yang di mana data tweet tersebut diambil dari empat daerah terbesar di Indonesia, yaitu Jakarta, Bandung, Medan, dan Surabaya. Setiap data akan diambil secara manual menggunakan Geolocation API yang telah di sediakan oleh Twitter melalui Twitter search. Hasil dari klasifikasi menggunakan algoritma Naïve Bayesian Classifier didapat 839 tweet positif, 32 tweet negatif, dan 67 tweet netral dari 938 tweet keseluruhan, atau dalam bentuk persentase ada 90% merupakan sentimen positif, 3% sentimen negatif, dan 7% sentimen netral terhadap bapak Joko Widodo. Dan 56 tweet positif, 6 tweet negatif, dan 8 tweet netral dari 70 tweet keseluruhan, atau dalam bentuk persentase ada 80% merupakan sentimen positif, 9% sentimen negatif, dan 11% sentimen netral terhadap bapak Prabowo. Tingkat akurasi yang dihasilkan dari algoritma Naïve Bayesian Classifier sendiri terhadap penelitian ini sebesar 77,62%.
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Rozi, Imam Fahrur, Elok Nur Hamdana, and Muhammad Balya Iqbal Alfahmi. "PENGEMBANGAN APLIKASI ANALISIS SENTIMEN TWITTER MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER (Studi Kasus SAMSAT Kota Malang)." Jurnal Informatika Polinema 4, no. 2 (February 1, 2018): 149. http://dx.doi.org/10.33795/jip.v4i2.164.

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Twitter adalah salah satu media sosial dimana pengguna dapat mencari topik tertentu dan membahas isu-isu terkini. Beberapa pesan singkat atau tweet dapat memuat opini terhadap produk dan layanan yang dirasakan oleh masyarakat. Data ini dapat menjadi sumber data untuk dijadikan objek penelitian. Penelitian ini bertujuan untuk membangun aplikasi analisis sentimen yang menerapkan pendekatan Naïve Bayes Classifier untuk mengklasifikasikan kata-kata dan difokuskan pada tweet dalam bahasa Indonesia. Data diperoleh melalui cara web scrapping dan sumber teks yang digunakan sebagai topik bahasan adalah Sistem Administrasi Manunggal Satu Atap (SAMSAT) Malang Kota. Proses klasifikasi dilakukan melalui serangkaian tahapan seperti preproses (case folding, cleaning, tokenizing, dan stopword) serta proses klasifikasi dengan algoritma Naïve Bayes Classifier itu sendiri untuk mendapatkan hasil klasifikasi dengan kategori positif, negatif atau netral. Berdasarkan hasil penelitian, algoritma Naïve Bayes Classifier memberikan unjuk kerja yang baik dalam analisis sentimen. Dari hasil uji akurasi klasifikasi yang dilakukan oleh aplikasi menghasilkan nilai akurasi tertinggi pada setiap kategori positif, negatif, netral masing-masing sebesar 82%, 92%, 80% dengan jumlah data latih 200 tweet negatif, 200 tweet positif, dan 200 tweet netral.
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Tarihoran, Yusran, and Kevin Jeremy Manurip. "Analisis Sentimen Pemilihan Gubernur Jawa Barat Tahun 2018 Dengan Aplikasi Twitter Menggunakan Metode Naïve Bayesian Classification." TeIKa 8, no. 1 (April 30, 2018): 99–105. http://dx.doi.org/10.36342/teika.v8i1.2243.

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Pemilihan Gubernur Jawa Barat 2018 ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Twitter. Semua orang bebas berpendapat atau beropini tentang calon Gubernur Jawa Barat 2018 sehingga memunculkan banyak opini, tidak hanya opini yang positif atau netral, adapula opini negatif. Media sosial khususnya Twitter sekarang ini menjadi salah satu tempat promosi atau kampanye yang efektif dan efisien untuk menggait para pendukung. Dalam hal ini peneliti akan melakukan riset terhadap salah satu tokoh publik yang mencalonkan diri gubernur Jawa Barat. Metode penelitian yang digunakan dalam riset kali ini adalah algoritma klasifikasi Naïve Bayesian Classifer. Data yang digunakan adalah tweet berbahasa Indonesia dengan kata kunci Ridwan Kamil (#RidwanKamil) sebanyak 1031 data tweet selamat setiap hari dimulai dari 15 Januari 2018 sampai 15 April 2018. Hasil dari klasifikasi menggunakan algoritma Naïve Bayesian Classifier didapat 690 jumlah tweet atau 67% dari jumlah keseluruhan data tweet yang mendukung bapak Ridwan Kamil atau bersifat positif khususnya terhadap program kerja yang akan dilakukan dan ini memberikan statistik probabilitas sebesar 73,13% tingkat akurasi Correctly Classified Instances.
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Dissertations / Theses on the topic "Tweet Classifier"

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Lemke, Steffen, and Athanasios Mazarakis. "Analyse wissenschaftlicher Konferenz-Tweets mittels Codebook und der Software Tweet Classifier." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234387.

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Mit seiner fokussierten Funktionsweise hat der Mikrobloggingdienst Twitter im Laufe des vergangenen Jahrzehnts eine beachtliche Präsenz als Kommunikationsmedium in diversen Bereichen des Lebens erreicht. Eine besondere Weise, auf die sich die gestiegene Sichtbarkeit Twitters in der täglichen Kommunikation häufig manifestiert, ist die gezielte Verwendung von Hashtags. So nutzen Unternehmen Hashtags um die auf Twitter stattfindenden Diskussionen über ihre Produkte zu bündeln, während Organisatoren von Großveranstaltungen und Fernsehsendungen durch Bekanntgabe ihrer eigenen, offiziellen Hashtags Zuschauer dazu ermutigen, den Dienst parallel zum eigentlichen Event als Diskussionsplattform zu nutzen. [... aus der Einleitung]
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Lemke, Steffen, and Athanasios Mazarakis. "Analyse wissenschaftlicher Konferenz-Tweets mittels Codebook und der Software Tweet Classifier." TUDpress, 2017. https://tud.qucosa.de/id/qucosa%3A30887.

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Mit seiner fokussierten Funktionsweise hat der Mikrobloggingdienst Twitter im Laufe des vergangenen Jahrzehnts eine beachtliche Präsenz als Kommunikationsmedium in diversen Bereichen des Lebens erreicht. Eine besondere Weise, auf die sich die gestiegene Sichtbarkeit Twitters in der täglichen Kommunikation häufig manifestiert, ist die gezielte Verwendung von Hashtags. So nutzen Unternehmen Hashtags um die auf Twitter stattfindenden Diskussionen über ihre Produkte zu bündeln, während Organisatoren von Großveranstaltungen und Fernsehsendungen durch Bekanntgabe ihrer eigenen, offiziellen Hashtags Zuschauer dazu ermutigen, den Dienst parallel zum eigentlichen Event als Diskussionsplattform zu nutzen. [... aus der Einleitung]
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Hall, Abraham. "Using Freebase, an Automatically Generated Dictionary, and a Classifier to Identify a Person's Profession in Tweets." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5788.

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Algorithms for classifying pre-tagged person entities in tweets into one of eight profession categories are presented. A classifier using a semi-supervised learning algorithm that takes into consideration the local context surrounding the entity in the tweet, hash tag information, and topic signature scores is described. In addition to the classifier, this research investigates two dictionaries containing the professions of persons. These two dictionaries are used in their own classification algorithms which are independent of the classifier. The method for creating the first dictionary dynamically from the web and the algorithm that accesses this dictionary to classify a person into one of the eight profession categories are explained next. The second dictionary is freebase, an openly available online database that is maintained by its online community. The algorithm that uses freebase for classifying a person into one of the eight professions is described. The results also show that classifications made using the automated constructed dictionary, freebase, or the classifier are all moderately successful. The results also show that classifications made with the automated constructed person dictionary are slightly more accurate than classifications made using freebase. Various hybrid methods, combining the classifier and the two dictionaries are also explained. The results of those hybrid methods show significant improvement over any of the individual methods.
M.S.
Masters
Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science
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Speriosu, Michael Adrian. "Semisupervised sentiment analysis of tweets based on noisy emoticon labels." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-3823.

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There is high demand for computational tools that can automatically label tweets (Twitter messages) as having positive or negative sentiment, but great effort and expense would be required to build a large enough hand-labeled training corpus on which to apply standard machine learning techniques. Going beyond current keyword-based heuristic techniques, this paper uses emoticons (e.g. ':)' and ':(') to collect a large training set with noisy labels using little human intervention and trains a Maximum Entropy classifier on that training set. Results on two hand-labeled test corpora are compared to various baselines and a keyword-based heuristic approach, with the machine learned classifier significantly outperforming both.
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Book chapters on the topic "Tweet Classifier"

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Menaria, Hemant Kumar, Pritesh Nagar, and Mayank Patel. "Tweet Sentiment Classification by Semantic and Frequency Base Features Using Hybrid Classifier." In First International Conference on Sustainable Technologies for Computational Intelligence, 107–23. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0029-9_9.

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Bhoi, Ashutosh, and Rakesh Chandra Balabantaray. "Hate Tweet Extraction from Social Media Text Using Autoencoder Wrapped Multinomial Naive Bayes Classifier." In Advances in Intelligent Systems and Computing, 619–28. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0171-2_59.

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Mourad, Sara S., Doaa M. Shawky, Hatem A. Fayed, and Ashraf H. Badawi. "Stance Detection in Tweets Using a Majority Vote Classifier." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), 375–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_37.

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Ram Vinay, Aishwarya, Mohsen Ali Alawami, and Hyoungshick Kim. "ConTheModel: Can We Modify Tweets to Confuse Classifier Models?" In Silicon Valley Cybersecurity Conference, 205–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72725-3_15.

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Angiani, Giulio, Stefano Cagnoni, Natalia Chuzhikova, Paolo Fornacciari, Monica Mordonini, and Michele Tomaiuolo. "Flat and Hierarchical Classifiers for Detecting Emotion in Tweets." In AI*IA 2016 Advances in Artificial Intelligence, 51–64. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49130-1_5.

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Nalini, K., and L. Jaba Sheela. "Classification of Tweets Using Text Classifier to Detect Cyber Bullying." In Advances in Intelligent Systems and Computing, 637–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13731-5_69.

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Pereira, João, Arian Pasquali, Pedro Saleiro, and Rosaldo Rossetti. "Transportation in Social Media: An Automatic Classifier for Travel-Related Tweets." In Progress in Artificial Intelligence, 355–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65340-2_30.

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Devi, Bali, Venkatesh Gauri Shankar, Sumit Srivastava, Kriti Nigam, and Lakshay Narang. "Racist Tweets-based Sentiment Analysis Using Individual and Ensemble Classifiers." In Micro-Electronics and Telecommunication Engineering, 555–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4687-1_52.

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Almatarneh, Sattam, Pablo Gamallo, Francisco J. Ribadas Pena, and Alexey Alexeev. "Supervised Classifiers to Identify Hate Speech on English and Spanish Tweets." In Digital Libraries at the Crossroads of Digital Information for the Future, 23–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34058-2_3.

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Manna, Sukanya, and Haruto Nakai. "Comparative Analysis of Different Classifiers on Crisis-Related Tweets: An Elaborate Study." In Nature-Inspired Computation in Data Mining and Machine Learning, 77–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28553-1_4.

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Conference papers on the topic "Tweet Classifier"

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Talbot, Ruth, Chloe Acheampong, and Richard Wicentowski. "SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification." In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/s15-2104.

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Prusa, Joseph, Taghi M. Khoshgoftaar, and Daivd J. Dittman. "Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data." In 2015 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2015. http://dx.doi.org/10.1109/iri.2015.49.

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Sharma, Nand Kishore, Surendra Rahamatkar, and Sachin Sharma. "Classification of Airline Tweet Using Naïve-Bayes Classifier for Sentiment Analysis." In 2019 International Conference on Information Technology (ICIT). IEEE, 2019. http://dx.doi.org/10.1109/icit48102.2019.00019.

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Saha, Tulika, Sriparna Saha, and Pushpak Bhattacharyya. "Tweet Act Classification : A Deep Learning based Classifier for Recognizing Speech Acts in Twitter." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851805.

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Demirbaga, Umit, and Devki Nandan Jha. "Social Media Data Analysis Using MapReduce Programming Model and Training a Tweet Classifier Using Apache Mahout." In 2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2). IEEE, 2018. http://dx.doi.org/10.1109/sc2.2018.00024.

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Giri, S. Jyothi, and Chandra Sekhar Vorugunti. "A novel online social network (Twitter)message (Tweet)classifier based on message diffusion in the network." In 2017 9th International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2017. http://dx.doi.org/10.1109/comsnets.2017.7945417.

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Stepchenkova, Svetlana, and Andrei Kirilenko. "Public opinion mining on Sochi-2014 Olympics." In CARMA 2016 - 1st International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/carma2016.2016.3102.

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The requirements of evidence-based policymaking promote interest to realtime monitoring of public’s opinions on policy-relevant topics, and social media data mining allows diversification of information portfolio used by public administrators. This study discusses issues in public opinion mining with respect to extraction and analysis of information posted on Twitter about Sochi-2014 Olympic. It focuses on topics discussed on Twitter and sentiment analysis of tweets about the Games. Final database contained 613,333 tweets covering time span from November 1, 2013 until March 31, 2014. Using hash tags the data were classified into the following categories: Events (21%); News (14%); Sports (12%); Anticipation of the Games (12%); Cheering of the teams (6%) and Problems &amp; Politics (2%). Research reveals considerable differences in the outcomes of machine sentiment classifiers: Deeply Moving, Pattern, and SentiStrength. SentiStrength produced the most suitable results in terms of minimization of incorrectly classified tweets. Methodological implications and directions for future research are discussed.
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Cui, Renhao, Gagan Agrawal, Rajiv Ramnath, and Vinh Khuc. "Ensemble of Heterogeneous Classifiers for Improving Automated Tweet Classification." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0151.

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Vougioukas, Michail, Ion Androutsopoulos, and Georgios Paliouras. "Identifying Retweetable Tweets with a Personalized Global Classifier." In SETN '18: 10th Hellenic Conference on Artificial Intelligence. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3200947.3201019.

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Saranya, G., G. Geetha, Chakrapani K, Meenakshi K, and S. Karpagaselvi. "Sentiment analysis of healthcare Tweets using SVM Classifier." In 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2020. http://dx.doi.org/10.1109/icpects49113.2020.9336981.

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