D by the data’s nonlinearity. Hence, the performance in the MLP classifier considerably improved the accuracy in the predictive process. An fascinating method focusing Icosabutate supplier around the attributes is presented in [15]. The authors hypothesized that the title’s grammatical building and also the abstract could emerge curiosity and attract readers’ focus. A new attribute, referred to as Gramatical Score, was proposed to reflect the title’s ability to attract users’ focus. To segment and markup words, they relied on the open-source tool Jieba [58]. The Grammatical Score is computed followed the actions below: Each and every sentence was divided into words separated by spaces; Each word received a grammatical label; The quantity of each and every word was counted in all products; Lastly, a table with words, labels, along with the quantity of words was obtained; Every single item receives a score with the Equation (10), exactly where gci represents the Grammatical Score on the ith item inside the dataset and k represents the kth word in the ith item. The n is the variety of words inside the title or summary. The weight may be the quantity of the kth word in all news articles, and count within this equation is the volume of the kth word within the ith item: gci =k =weight(k) count(k)n(10)Sensors 2021, 21,15 ofIn addition to this attribute, the authors utilized a logarithmic transformation and normalization by developing two new attributes: categoryscore and authorscore: categoryscore = n ln(sc ) n (11)The categoryscore may be the average view for each category. The variable n within the Equation (11) represents the total variety of news articles of each author. For each category, the information that belonged to this category were selected, and Equation (11) was utilized: authorscore = m ln(s a ) m (12)The authorscore is defined in Equation (12), exactly where m represents the total number of news articles of every single author. Ahead of calculating the authorscore, data are grouped by author. For the prediction, the authors utilized the titles and abstracts’ length and temporal attributes moreover towards the three pointed out attributes. The authors’ objective was to predict no matter if a news post could be popular or not. For this, they used the freebuf [59] internet site as a information source. They collected the products from 2012 to 2016, and two classes have been defined: common and unpopular. As these classes are unbalanced and popular articles would be the minority, the metric AUC was utilised, that is much less influenced by the distribution of unbalanced classes. Additionally, the kappa coefficient was utilised, that is a statistical measure of agreement for nominal scales [60]. The authors chosen 5 ranking algorithms to observe the most beneficial algorithm for predicting the reputation of news articles: Random Forest, Selection Tree J48, ADTree, Naive Bayes, and Bayes Net. We identified that the ADTree algorithm has the best overall performance with 0.837 AUC, as well as the kappa coefficient equals 0.523. Jeon et al. [40] proposed a hybrid model for reputation prediction and applied it to a true video dataset from a Korean Streaming service. The proposed model divides videos into two categories, the first category, referred to as A, consisting of videos that have previously had connected work, by way of example, tv series and MAC-VC-PABC-ST7612AA1 Cancer weekly Tv programs. The second category, called B, is videos which might be unrelated to earlier videos, as within the case of films. The model makes use of distinct characteristics for every type. For sort A, the authors use structured data from earlier contents, which includes the amount of views. For sort B, they use unstruct.