Twitter news classification: Theoretical and practical comparison of SVM against Naive Bayes algorithms

Dilrukshi, Inoshika; Zoysa, Kasun De

doi:10.1109/icter.2013.6761192

Cited by 16 publications

(6 citation statements)

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“…This is contrary to NB, which is simpler and has a significantly lower memory requirement. Further, larger training sets significantly improve the accuracy of NB, unlike SVM, which already has a high benchmark [41] [42].…”

Section: B Support Vector Machine (Svm)mentioning

confidence: 99%

Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques

ALSaif¹,

Al-Dossari²

2018

ijacsa

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Crime analysis has become a critical area for helping law enforcement agencies to protect civilians. As a result of a rapidly increasing population, crime rates have increased dramatically, and appropriate analysis has become a timeconsuming effort. Text mining is an effective tool that may help to solve this problem to classify crimes in effective manner. The proposed system aims to detect and classify crimes in Twitter posts that written in the Arabic language, one of the most widespread languages today. In this paper, classification techniques are used to detect crimes and identify their nature by different classification algorithms. The experiments evaluate different algorithms, such as SVM, DT, CNB, and KNN, in terms of accuracy and speed in the crime domain. Also, different features extraction techniques are evaluated, including rootbased stemming, light stemming, n-gram. The experiments revealed the superiority of n-gram over other techniques. Specifically, the results indicate the superiority of SVM with trigram over other classifiers, with a 91.55% accuracy.

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Section: B Support Vector Machine (Svm)mentioning

confidence: 99%

Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques

ALSaif¹,

Al-Dossari²

2018

ijacsa

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“…Due to its simplicity, speed and accuracy in large databases, NB has become a popular classifier in many applications [24,28]. The accuracy of the NB classifier is comparable to the ANN [29] but lesser than that of the SVM [30][31][32].…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

Partial Histogram Bayes Learning Algorithm for Classification Applications

Lawend

Muad²,

Hussain³

2018

IJET

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This paper presents a proposed supervised classification technique namely partial histogram Bayes (PHBayes) learning algorithm. Conventional classifier based on Gaussian function has limitation when dealing with different probability distribution functions and requires large memory for large number of instance. Alternatively, histogram based classifiers are flexible for different probability density function. The aims of PHBayes are to handle large number of instances in datasets with lesser memory requirement, and fast in training and testing phases. The PHBayes depends on portion of the observed histogram that is similar to the probability density function. PHBayes was analyzed using synthetic and real data. Several factors affecting classification accuracy were considered. The PHBayes was compared with other established classifiers and demonstrated higher accurate classification, lesser memory even when dealing with large number of instance, and faster in training and testing phases.

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“…Dilrukshi and De Zoysa [26] developed a news classifcation and categorization system based on stratifying news by relevance. Te study utilized a Twitter dataset and focused on articles related to Sri Lanka, utilizing dimensionreduction techniques.…”

Section: Introductionmentioning

confidence: 99%

News Classification and Categorization with Smart Function Sentiment Analysis

Nkongolo Wa Nkongolo

2023

International Journal of Intelligent Systems

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Search engines are tools used to find information on the Internet. Since the web has a plethora of websites, the engine queries the majority of active sites and builds a database organized according to keywords utilized in the search. Because of this, when a user types a few descriptive words on the home page of the search engine, the search function lists websites corresponding to these keywords. However, there are some problems with this search approach. For instance, if a user wants information about the word Jaguar, most search results are animals and cars. This is a polysemic problem that forces search engines to always provide the most popular but not the most relevant results. This article presents a study of using sentiment technology to help news classification and categorization and improve the classification accuracy. We have introduced a smart search function embedded into a search engine to tackle polysemic issues and record relevant results to determine their sentimentality. Therefore, this study presents a topic that involves several aspects of natural language processing (NLP) and sentiment analysis for news categorization and classification. A web crawler was used to collect British Broadcasting Corporation (BBC) news across the Internet, carried out preprocessing of text by using NLP, and applied sentiment analysis methods to determine the polarity of the processed text data. The sentimentality represents negative, positive, or neutral polarities assigned by the sentiment analysis algorithms. The research utilized the BBC news site to collect different information using a web crawler and a database to explore the sentimentality of BBC news. The natural language toolkit (NLTK) and BM25 indexed and preprocessed patterns in the database. The experimental results depict the proposed search function surpassing normal search with an accuracy rate of 85%. Moreover, the results show a negative polarity of BBC news using the Sentistrength algorithm. Furthermore, the Valence Aware Dictionary and sEntiment Reasoner (VADER) was the best-performing sentiment analysis model for news classification. This model obtained an accuracy of 85% using data collected with the proposed smart function.

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Twitter news classification: Theoretical and practical comparison of SVM against Naive Bayes algorithms

Cited by 16 publications

References 0 publications

Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques

Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques

Partial Histogram Bayes Learning Algorithm for Classification Applications

News Classification and Categorization with Smart Function Sentiment Analysis

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