The development of the internet today is growing very rapidly which indirectly encourages the creation of personal web content that involves sentiments such as blogs, tweets, web forums and other types of social media. Humans often make decisions based on input from friends, relatives, colleagues and others. Supported by the availability of growth and popularity of opinion-rich resources or sentiments such as online site reviews for ecommerce products and personal blogs For example, the expression of personal feelings that allows users to discuss everyday problems, exchange political views, evaluate services and products like Smartphone’s Smart TV’s etc. This research applies opinion mining method by using Naïve Bayes Classifier and Information Gain algorithm based on Feature Selection. Testing this method uses the ECommerce based tweet dataset downloaded from the Twitter Cloud Repository. The purpose of this study is to improve the accuracy of the Naïve Bayes algorithm in classifying documents along with Information Gain methodology. Accuracy achieved in this study amounted to 88.80% which is appropriate to evaluate the sentiments.

Sentiment Analysis using Naive Bayes Classifier and Information Gain Feature Selection over Twitter

Many water utilities are currently struggling to manage their aging infrastructure. Water mains are a key component of water systems, as they convey drinking water to billions of end-users worldwide. However, as they are usually buried underground, their visual inspection and condition assessment can be cumbersome. Furthermore, water main failure may lead to significant challenges for utilities and end-users, such as service interruption, capacity reduction, as well as high replacement and rehabilitation costs. Accordingly, various researchers have sought to develop statistical methods to predict water main condition. Previous studies have developed models for single systems, applying a range of statistical and machine learning methods, from linear regression to artificial neural networks. The objective of the present study is to compare the applicability and accuracy of a few machine-learning algorithms, such as Random Forest, Logistic Regression and Decision Tree to predict whether a pipe is going to fail or not (Classification). Data from two Canadian municipalities has been collected (Saskatoon, Saskatchewan and Waterloo, Ontario). A number of features are taken into consideration, such as diameter, age, material, and the number of previous failures. The results show a moderate to high accuracy of classification models although in some cases the performance of models is relatively low. Thus, deeper data mining approaches with higher concentrations on the most influential attributes would increase the reliability of the models. 

Comparison of Machine Learning Classifiers for Predicting Water Main Failure

 Abstract — Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player’s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression

various Injury Prediction for Soccer Players Using Machine

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Sentiment Analysis using Naive Bayes Classifier and Information Gain Feature Selection over Twitter

Comparison of Machine Learning Classifiers for Predicting Water Main Failure

various Injury Prediction for Soccer Players Using Machine

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