Text Mining for Cyberbullying Detection: a Brazilian Portuguese Evaluation

Bullying and cyberbullying are words commonly seen in today's news. Although the scientific community has evaluated text mining techniques for cyberbullying detection, few studies have targeted Brazilian Portuguese datasets. Our study aims to assess the text mining application to detect cyberbullying messages written in Brazilian Portuguese. We gathered posts and comments from Reddit communities and extracted several text features. We then processed these features using Naïve Bayes and SVM classifiers to uncover cyberbullying activity. The outcomes of this experiment may not be used solo for cyberbullying detection; however, they can aid moderators in prioritizing content reviews and acting faster on real cyberbullying cases.

The evolution of knowledge on genes associated with human diseases

Thousands of scientific articles describing genes associated with human diseases are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprised of 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our approach helped to unravel the molecular bases of diseases over time, and to detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes which are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating in this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.

Customer Sentiments towards Fin-Tech Apps in India: A Text Mining Application

Fintech sector has witnessed incredible growth in India with the government promoting digital and cashless transactions along with the penetration of smartphones and internet connectivity in the country. Online reviews and customer opinions play a key role in the choice of Fintech apps among users. The customers compare the services of these service providers based on online reviews and ratings to finalize their choice. Fintech companies use this data to improve their customer experience. In this paper we attempt to provide useful insights into the customer sentiments towards Fintech apps in India by using a text mining approach. By analyzing customer opinions and reviews, we attempt to understand the acceptance of the services provided by the Fintech companies in India. We have used sentiment analysis to classify positive, negative and neutral reviews to understand the user sentiment towards Fintech apps. We also put forward suggestions to address the gaps in the services provided by these Fintech companies based on our analysis and findings.

Linking Twitter Sentiment Knowledge with Infrastructure Development

Sentiment analysis is related to the automatic extraction of positive or negative opinions from the text. It is a special text mining application. It is important to classify implicit contents from citizen’s tweet using sentiment analysis. This research aimed to find out the opinion of infrastructure that sustained urban development in Surabaya, Indonesia’s second largest city. The procedures of text mining analysis were the data undergoes some preprocessing first, such as removing the link, retweet (RT), username, punctuation, digits, stopwords, case folding, and tokenizing. Then, the opinion was classified into positive and negative comments. Classification methods used in this research were support vector machine (SVM) and neural network (NN). The result of this research showed that NN classification method was better than SVM.

Best Approximate of Vector Space Model by Using SVD

A quick growth of internet technology makes it easy to assemble a huge volume of data as text document; e. g., journals, blogs, network pages, articles, email letters. In text mining application, increasing text space of datasets represent excessive task which makes it hard to pre-processing documents in efficient way to prepare it for text mining application like document clustering. The proposed system focuses on pre-processing document and reduction document space technique to prepare it for clustering technique. The mutual method for text mining problematic is vector space model (VSM), each term represent a features. Thus the proposed system create vector-space mod-el by using pre-processing method to reduce of trivial data from dataset. While the hug dimen-sionality of VSM is resolved by using low-rank SVD. Experiment results show that the proposed system give better document representation results about 10% from previous approach to prepare it for document clustering