Textual Classification for Sentiment Detection. Brand Reputation Analysis on the Web using Natural Language Processing and Machine Learning

Academic Paper from the year 2018 in the subject Computer Science - Applied University of the Witwatersrand course: Machine learning - Artificial Intelligence - Big Data - Natural Language Processing language: English abstract: Cloud computing makes it possible to build scalable machine learning systems for processing massive amounts of complex data be them structured or unstructured real-time or historical the so-called Big Data. Publicly available cloud computing platforms have been made available for instance Amazon EC2 EMR and Google Compute Engine. More importantly open source APIs and libraries have also been developed for ease of programming on the cloud for instance Cascading Storm Scalding Apache Spark and Trackur. Meanwhile computational intelligence approaches examples of which include evolutionary computation immune-inspired approaches and swarm intelligence are also employed to develop scalable machine learning and data analytics tools. In this project we presented the sentiment-focused web crawling problem and designed a sentiment-focused web crawler frame-work for faster discovery and retrieval of sentimental context on the Web. We have developed a computational framework to perform automated reputation analysis on the Web using Natural Language Processing and Machine Learning. This paper introduces such framework and tests its performance on automated sentiment analysis for brand reputation. In addition we proposed different strategies for predicting the polarity scores of web pages.Experiments have shown that the performance of our proposed framework is more efficient than existing frameworks. Reputation analysis is a useful application for organizations that are looking for people's opinions about their products and services. Our approach consists of 4 parts: in the first part the framework performed Web crawling based on the query specified by the user. In the second part the framework locates relevant information w