If the question'What is sentiment analysis?' popped up in your mind as you clicked on this blog, I think you will find my first blog in this series interesting. Essentially, sentiment analysis is a natural language processing technique used to determine the emotional tone of textual data. It is primarily used to understand customer satisfaction, and gauge brand reputation, call center interactions as well as customer feedback and messages. There are various types of sentiment analysis that are common in the real world. In this part of my blog series, let me walk you through the implementation of sentiment analysis.

Laird Harrison writes about science, health and culture. His work has appeared in national magazines, in newspapers, on public radio and on websites. He is at work on a novel about alternate realities in physics. Harrison teaches writing at the Writers Grotto.

In 1945 the volume of human knowledge doubled every 25 years. Now, that number is 12 hours [1]. With our collective computational power rapidly increasing, vast amounts of data and our ability to assimilate it, has seeded unprecedented fertile ground for innovation. Healthtech companies are rapidly sprouting from data ridden soil at exponential rates. Cell free DNA companies, once a rarity, are becoming ubiquitous. The genomics landscape, once dominated by the few, are being inundated by a slew of competitors. Grandiose claims of being able to diagnose 50 different cancers from a single blood sample, or use AI to best dermatologists, radiologists, pathologists, etc., are being made at alarming rates. Accordingly, it's imperative to know how to assess these claims as fact or fiction, particularly when such claimants may employ "statistical misdirection". In this addition to "The Insider's Guide to Translational Medicine" we disarm perpetrators of statistical warfare of their greatest ...

The Receiver Operating Characteristic -- Area Under the Curve (ROC-AUC) measure is widely used to assess the performance of binary classifiers. However, sometimes, it is more appropriate to evaluate your classifier based on measuring the Area Under the Precision-Recall Curve (AUPRC). We will present a detailed comparison between these two measures, accompanied by empirical results and graphical illustrations. Scikit-learn experiments are also available in a corresponding notebook. I'll assume you're familiar with precision and recall and the elements of the confusion matrix (TP, FN, FP, TN).

In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains an ensemble of models. In recent years, several approaches have been proposed for training ensembles, and conflicting views prevail with regards to the importance of various ingredients of these approaches. In this paper, we aim to address the benefits of two ingredients -- prior functions and bootstrapping -- which have come into question. We show that prior functions can significantly improve an ensemble agent's joint predictions across inputs and that bootstrapping affords additional benefits if the signal-to-noise ratio varies across inputs. Our claims are justified by both theoretical and experimental results.

Abstract-Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk, spam emails and SMSs are causing major resource wastage by unnecessarily flooding the network links. Although most spam mail originate with advertisers looking to push their products, some are much more malicious in their intent like phishing emails that aims to trick victims into giving up sensitive information like website logins or credit card information this type of cybercrime is known as phishing. To countermeasure spams, many researches and efforts are done to build spam detectors that are able to filter out messages and emails as spam or ham. In this research we build a spam detector using BERT pre-trained model that classifies emails and messages by understanding to their context, and we trained our spam detector model using multiple corpuses like SMS collection corpus, Enron corpus, SpamAssassin corpus, Ling-Spam corpus and SMS spam collection corpus, our spam detector performance was 98.62%, 97.83%, 99.13% and 99.28% respectively.

Crypto.com, Microsoft, NVidia, and Okta all got hacked this year. In some hacks, attackers are looking to take data, while some are just trying things out. Either way, it is in the interest of companies to patch up the holes in their security systems as more attackers are learning to take advantage of them. The project I am working on now is one to prevent cyber threats like these from happening. When a company is hacked, there is a lot at stake.

There are strong incentives to build models that demonstrate outstanding predictive performance on various datasets and benchmarks. We believe these incentives risk a narrow focus on models and on the performance metrics used to evaluate and compare them -- resulting in a growing body of literature to evaluate and compare metrics. This paper strives for a more balanced perspective on classifier performance metrics by highlighting their distributions under different models of uncertainty and showing how this uncertainty can easily eclipse differences in the empirical performance of classifiers. We begin by emphasising the fundamentally discrete nature of empirical confusion matrices and show how binary matrices can be meaningfully represented in a three dimensional compositional lattice, whose cross-sections form the basis of the space of receiver operating characteristic (ROC) curves. We develop equations, animations and interactive visualisations of the contours of performance metrics within (and beyond) this ROC space, showing how some are affected by class imbalance. We provide interactive visualisations that show the discrete posterior predictive probability mass functions of true and false positive rates in ROC space, and how these relate to uncertainty in performance metrics such as Balanced Accuracy (BA) and the Matthews Correlation Coefficient (MCC). Our hope is that these insights and visualisations will raise greater awareness of the substantial uncertainty in performance metric estimates that can arise when classifiers are evaluated on empirical datasets and benchmarks, and that classification model performance claims should be tempered by this understanding.

Cars are central to the cinema in a variety of ways. While the railroad and trains were prominent during the silent era -- and in the westerns that continued to be produced well into the 1970s -- automobiles offer greater freedom of movement than trains do and thus offer greater cinematic possibilities. So extensive is this relationship that the car chase has almost become a mini-genre unto itself. Yet film scholars have not yet dedicated any work to exploring this subject in depth. But we can start by examining the relationship between cinema and transportation more broadly.

As we begin to grow the MILKit community organically, it's important to keep the marketing message clear and accurate. M.I.L.K. an acronym for Machine Intelligence Launch Knowledge, which describes our machine learning objective. The utility we're building for the crypto community is unique. Not just another DEX, Swap, P2E metaverse game but a vitally important utility to help protect people from scams, rug-pulls, honeypots and other blockchain hazards. This article will be a living document, which will be updated to include answers to questions that arise from the community.