Discriminative training of naive Bayes classifiers for natural language call routing

Abstract Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest.

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Cyber Bullying Detection for Twitter Using ML Classification Algorithms

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38701 ◽

2021 ◽

Vol 9 (11) ◽

pp. 24-29

Author(s):

Muskan Patidar

Keyword(s):

Machine Learning ◽

Social Media ◽

Natural Language ◽

Naive Bayes ◽

Learning Algorithms ◽

Naïve Bayes ◽

Cyber Bullying ◽

Machine Learning Algorithms ◽

Support Vector ◽

Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

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Improving Text Categorization by Multicriteria Feature Selection

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2005.p0570 ◽

2005 ◽

Vol 9 (5) ◽

pp. 570-575

Author(s):

Son Doan ◽

◽

Susumu Horiguchi ◽

Keyword(s):

Feature Selection ◽

Natural Language ◽

Text Categorization ◽

Naive Bayes ◽

Naïve Bayes ◽

Experimental Results ◽

Benchmark Data ◽

Bayes Algorithm

Text categorization involves assigning a natural language document to one or more predefined classes. One of the most interesting issues is feature selection. We propose an approach using multicriteria ranking of eatures, a new procedure for feature selection, and apply these to text categorization. Experimental results dealing with Reuters-21578 and 20Newsgroups benchmark data and the naive Bayes algorithm show that our proposal outperforms conventional feature selection in text categorization performance.

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Twitter based Sentiment Analysis of Impact of Covid-19 on Education Globaly

International Journal of Artificial Intelligence & Applications ◽

10.5121/ijaia.2021.12302 ◽

2021 ◽

Vol 12 (03) ◽

pp. 15-24

Author(s):

Swetha Sree Cheeti ◽

Yanyan Li ◽

Ahmad Hadaegh

Keyword(s):

Natural Language ◽

Sentiment Analysis ◽

Education System ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

The World ◽

The Impact

Education system has been gravely affected due to widespread of Covid-19 across the globe. In this paper we present a thorough sentiment analysis of tweets related to education available on twitter platform and deduce conclusions about its impact on people’s emotions as the pandemic advanced over the months. Through twitter over ninety thousand tweets have been gathered related to the circumstances involving the change in education system over the world. Using Natural language tool kit (NLTK) functionalities and Naive Bayes Classifier a sentiment analysis has been performed on the gathered dataset. Based on the results of this analysis we infer to exhibit the impact of covid-19 on education and how people’s sentiment altered due to the changes with regard to the education system. Thus, we would like to present a better understanding of people’s sentiment on education while trying to cope with the pandemic in such unprecedented times.

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Analisis sentimen pada Twitter dengan menggunakan metode Naïve Bayes Classifier

JNANALOKA ◽

10.36802/jnanaloka.2020.v1-no2-81-86 ◽

2021 ◽

pp. 81-86

Author(s):

Sigit Suryono ◽

Emha Taufiq Luthfi

Keyword(s):

Text Mining ◽

Natural Language ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier

Analisis Sentiment merupakan salah satu cabang dari bidang ilmu Text Mining. Analisis sentiment merupakan sumber penting dalam melakukan evaluasi dan pengambilan keputusan terhadap sebuah topik permasalahan. Tujuan utama dari analisis sentiment adalah untuk mengetahui polaritas dari sentiment positif, negatif ataupun netral. Sentiment-sentiment tersebut salah satunya didapatkan dari Twitter. Dalam tulisan ini, tweet-tweet yang berhubungan dengan kata kunci yang dicari dikumpulkan dari Twitter dengan menggunakan API Twitter dan data mentah yang didapatkan diolah dengan menggunakan Natural Language Toolkit pada bahasa pemrograman Python. Setelah diolah selanjutnya akan dilakukan klasifikasi dengan menggunakan Naïve Bayes Classifier untuk mengetahui tingkat akurasi dari proses klasifikasi yang dilakukan. Proses klasifikasi dilakukan dengan RapidMiner. Dari hasil uji coba sebanyak empat kali, didapatkan hasil tingkat akurasi pada percobaan pertama sebesar 62.98%, percobaan kedua sebesar 64.95%, percobaan ketiga sebesar 66.36%, dan percobaan keempat sebesar 66.79%. Dari hasil klasifikasi didapat tingkat persentase sentiment positif sebesar 28%, sentiment negatif sebesar 20% dan sentiment netral sebesar 52%.

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Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter

Journal of Dinda : Data Science, Information Technology, and Data Analytics ◽

10.20895/dinda.v1i1.180 ◽

2021 ◽

Vol 1 (1) ◽

pp. 42-51

Author(s):

Ardianne Luthfika Fairuz ◽

Rima Dias Ramadhani ◽

Nia Annisa Ferani Tanjung

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

K Nearest Neighbor

Akhir tahun 2019 lalu dunia digemparkan oleh munculnya suatu penyakit yang disebabkan oleh virus SARS-CoV-2 yang merupakan jenis virus terbaru dari coronavirus. Penyakit ini dikenal dengan nama COVID-19. Penyebaran penyakit ini terbilang cukup luas dan cepat. Dalam waktu singkat penyakit ini mulai menyebar ke segala penjuru dunia tak terkecuali Indonesia. Dengan tingkat penyebaran yang begitu tinggi dan belum ditemukannya vaksin untuk COVID-19, menyebabkan kekacauan di tengah masyarakat. Hal ini mempengaruhi banyak sektor kehidupan masyarakat. Tak sedikit masyarakat yang aktif bersosial media dan menuliskan pendapat, opini serta pemikirannya di platform media sosial seperti Twitter. Terjadinya pandemi ini mendorong masyarakat untuk menuliskan opini, pemikiran serta pendapatnya terhadap COVID-19 pada media sosial Twitter. Dibutuhkan suatu model sentiment analysis untuk mengklasifikasi tweet masyarakat di Twitter menjadi positif dan negatif. Sentiment analysis merupakan bagian dari Natural Language Processing yang membuat sebuah sistem guna mengenali serta mengekstraksi opini dalam bentuk teks. Pada penelitian ini digunakan algoritma Naive Bayes dan K-Nearest Neighbor untuk digunakan dalam membangun model sentiment analysis terhadap tweet pengguna Twitter terhadap COVID-19. Didapatkan akurasi sebesar 85% untuk algoritma Naïve Bayes dan 82% untuk algoritma K-Nearest Neighbor pada nilai k=6, 8, dan 14.

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