scholarly journals Does SEO Matter for Startups? Identifying Insights from UGC Twitter Communities

Informatics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 47
Author(s):  
José Ramón Saura ◽  
Ana Reyes-Menendez ◽  
Chris Van Nostrand
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Search Engine ◽  
Latent Dirichlet Allocation ◽  
Clustering Algorithms ◽  
User Generated Content ◽  
Digital Marketing ◽  
Analysis Process ◽  
User Communities ◽  
Technique Optimization

In the present study, we analyzed User Generated Content (UGC) to measure the importance of Search Engine Optimization (SEO) for startups. For this purpose, we used several clustering algorithms to identify user communities on Twitter. The dataset contained a total of 67,126 tweets. A three-step UGC analysis process was applied to the data. First, a Latent Dirichlet allocation (LDA) was developed to divide the UGC-sample into topics. Next, a sentiment analysis (SA) with machine-learning was applied to divide the sample of topics into negative, positive, and neutral feelings. Finally, a textual analysis (TA) process with data mining techniques was used to extract indicators related to the SEO technique optimization in startups. The results helped us identify UGC communities in Twitter about SEO for startups and the main optimization indicators according to the feelings expressed in tweets. Our results also demonstrated that Black Hack SEO is not the most relevant strategy of positioning of digital marketing for startups and that, although this strategy is used by the startups, it is predominantly negatively perceived by SEO UGC communities.

scholarly journals Improved minimum-minimum roughness algorithm for clustering categorical data

2021 ◽  
Vol 8 (10) ◽  
pp. 43-50
Author(s):  
Truong et al. ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Hierarchical Clustering ◽  
Categorical Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Data Sets ◽  
Top Down ◽  
Hierarchical Clustering Algorithm

Clustering is a fundamental technique in data mining and machine learning. Recently, many researchers are interested in the problem of clustering categorical data and several new approaches have been proposed. One of the successful and pioneering clustering algorithms is the Minimum-Minimum Roughness algorithm (MMR) which is a top-down hierarchical clustering algorithm and can handle the uncertainty in clustering categorical data. However, MMR tends to choose the category with less value leaf node with more objects, leading to undesirable clustering results. To overcome such shortcomings, this paper proposes an improved version of the MMR algorithm for clustering categorical data, called IMMR (Improved Minimum-Minimum Roughness). Experimental results on actual data sets taken from UCI show that the IMMR algorithm outperforms MMR in clustering categorical data.

scholarly journals Ensemble Hybrid K- Means and DBSCAN Clustering Algorithm – HDKA for Cancer Dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 6036-6040
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Cancer Dataset ◽  
Dbscan Clustering ◽  
Selection Of

Data Mining is the foremost vital space of analysis and is pragmatically utilized in totally different domains, It becomes a highly demanding field because huge amounts of data have been collected in various applications. The database can be clustered in more number of ways depending on the clustering algorithm used, parameter settings and other factors. Multiple clustering algorithms can be combined to get the final partitioning of data which provides better clustering results. In this paper, Ensemble hybrid KMeans and DBSCAN (HDKA) algorithm has been proposed to overcome the drawbacks of DBSCAN and KMeans clustering algorithms. The performance of the proposed algorithm improves the selection of centroid points through the centroid selection strategy.For experimental results we have used two dataset Colon and Leukemia from UCI machine learning repository.

Simulate, Optimise, Partition: Algorithmic Diagrams of Pattern Recognition from 1953 Onwards

2016 ◽  
Author(s):  
Adrian Mackenzie
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Deep Learning ◽  
Clustering Algorithms ◽  
Flow Through ◽  
The Cold War ◽  
Hand Writing

Contemporary attempts to find patterns in data, ranging from the now mundane technologies of hand-writing recognition through to mammoth infrastructure-heavy practices of deep learning conducted by major business and government actors, rely on a group of techniques intensively developed during the 1950-60s in physics, engineering and psychology. Whether we designate them as pattern recognition, data mining, or machine learning, these techniques all seek to uncover patterns in data that cannot appear directly to the human eye, either because there are too many items for anyone to look at, or because the patterns are too subtly woven through in the data. From the techniques in current use, three developed in the Cold War era iconify contemporary modes of pattern finding: Monte Carlo simulation, gradient descent, and clustering algorithms that search for groups or clusters in data. Each of these techniques implements a different mode of pattern, and these different modes of pattern recognition flow through into contemporary scientific, technological, business and governmental problematizations. The different perspectives on event, trajectory, and proximity they embody imbue many power relations, forms of value and the play of truth/falsehood today.

scholarly journals Comparing Data-Driven Methods for Extracting Knowledge from User Generated Content

2019 ◽  
Vol 5 (4) ◽  
pp. 74 ◽  
Author(s):  
Jose Ramon Saura ◽  
Ana Reyes-Menendez ◽  
Ferrão Filipe
Keyword(s):  
Complex Networks ◽  
Latent Dirichlet Allocation ◽  
Knowledge Extraction ◽  
Marketing Strategies ◽  
User Generated Content ◽  
Digital Marketing ◽  
Analysis Technique ◽  
Three Phase ◽  
Networks Analysis ◽  
Visualization Techniques

This study aimed to compare two techniques of business knowledge extraction for the identification of insights related to the improvement of digital marketing strategies on a sample of 15,731 tweets. The sample was extracted from user generated content (UGC) from Twitter using two methods based on knowledge extraction techniques for business. In Method 1, an algorithm to detect communities in complex networks was applied; this algorithm, in which we applied data visualization techniques for complex networks analysis, used the modularity of nodes to discover topics. In Method 2, a three-phase process was developed for knowledge extraction that included the application of a latent Dirichlet allocation (LDA) model, a sentiment analysis (SA) that works with machine learning, and a data text mining (DTM) analysis technique. Finally, we compared the results of each of the two techniques to see whether or not the results yielded by these two methods regarding the analysis of companies’ digital marketing strategies were mutually complementary.

scholarly journals Opinion Based Ranking System using Machine Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 360-363
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Unstructured Data ◽  
Scoring Functions ◽  
Online Services ◽  
Ranking System ◽  
Analysis Process

The rapid increase in the online services in the recent years. Everyone sending feedback/review after used particular services. For unstructured data it released active countless opportunity ties and challenges for data mining research. This paper is especially for reviews of hotels which are given by various hotels visitors. Reviews are posted as a comment only. It is difficult to identify the positive & negative review. We used dataset of different hotels and perform sentiment analysis process. For classification word to Vec Algorithm is being used. Forthe positive and negative review calculation r2 & f1 scoring functions are very useful.

Clustering Techniques

2019 ◽  
pp. 198-223 ◽  
Author(s):  
Harendra Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Image Analysis ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Main Task ◽  
Clustering Techniques ◽  
Data Points ◽  
Exploratory Data ◽  
Exploratory Data Mining

Clustering is a process of grouping a set of data points in such a way that data points in the same group (called cluster) are more similar to each other than to data points lying in other groups (clusters). Clustering is a main task of exploratory data mining, and it has been widely used in many areas such as pattern recognition, image analysis, machine learning, bioinformatics, information retrieval, and so on. Clusters are always identified by similarity measures. These similarity measures include intensity, distance, and connectivity. Based on the applications of the data, different similarity measures may be chosen. The purpose of this chapter is to produce an overview of much (certainly not all) of clustering algorithms. The chapter covers valuable surveys, the types of clusters, and methods used for constructing the clusters.

Dimensionality Reduction of Production Data Using PCA and DBSCAN Techniques

2020 ◽  
Author(s):  
Umadevi S ◽  
NirmalaSugirthaRajini
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Dimensionality Reduction ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Production Data ◽  
Related Data ◽  
Dbscan Clustering ◽  
Analysis Process ◽  
Research Article

Now a day’s data mining concepts are applied in various fields like medical, agriculture, production, etc. Creation of cluster is one of the major problems in data analysis process. Various clustering algorithms are used for data analysis purpose which depends upon the applications. DBSCAN is the famous method to create cluster. This article describes DBSCAN clustering concept applied on production database. The main objective of this research article is to collect and group the related data from large amount of data and remove the unwanted data. This clustering algorithm removes the unwanted attributes and groups the related data based upon density value.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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