Data analytics

3 Ways to Apply Latent Semantic Analysis on Large-Corpus Text on macOS Terminal, JupyterLab, and Colab

Latent semantic analysis works on large-scale datasets to generate representations to discover the insights through natural language processing. There are different approaches to perform the latent semantic analysis at multiple levels such as document level, phrase level, and sentence level. Primarily semantic analysis can be summarized into lexical semantics and the study of combining individual words into paragraphs or sentences. The lexical semantics classifies and decompo...

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AI Trained to Perform Sentiment Analysis on Amazon Electronics Reviews in JupyterLab

The recent advancements in natural language processing over last few decades allowed a significant number of enterprises to leverage the potential of sentiment analysis by training artificial intelligence through deep learning and machine learning algorithms to teach the machine and perform particular operations similar to what humans apply through cognitive abilities. Though, the machines cannot comprehend, perceive, and process the scenarios through life-like experience, ad...

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Building SMS SPAM Detector and Generating a WordCloud with Kaggle Dataset in JupyterLab

Background problem At least 97% of American use text messages over mobile phones every day. In 2016, according to the research conducted by Portio research, 8.3 trillion messages exchanged over the mobile phones. The rising flood of big data shows an exchange of 23 billion messages per day and 16 million messages per minute. There are around 6.4 billion mobile subscribers around the world by the end of 2012. According to Portio Research, there will be a CAGR growth of 4.8% o...

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Building Email SPAM Detector with Naïve Bayes and AdaBoost Machine Learning Classifiers in JupyterLab

Natural language processing is a sub-branch of artificial intelligence. Building a machine or a tool to process the data through natural language processing requires mathematics, statistics, algorithms, and Python programming. Advanced techniques such as Word2Vec can convert words into vectors which makes it easier to process the text through mathematics and deep learning algorithms. Python language can handle the language humans speak, write, and understand. Before we begin ...

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R has knives out for IBM SPSS and SAS

Introduction ​ Originally Bell Labs has conceived the idea of language S in the mid-1970s to resolve data analytics and statistical conundrums. The purpose of the implementation project was to perform statistical analysis of their corporation leveraging the libraries of Fortran language. The invention of S language did not include the functions needed for statistical computing. In the late 1980s, the act of rebuilding the source code in language C reinvented S languag...

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