"Learning to choose is hard. Learning to choose well is harder. And learning to choose well in a world of unlimited possibilities is harder still, perhaps too hard." When starting a new NLP sentiment analysis project, it can be quite an overwhelming task to narrow down on a select methodology for a given application. Do we use a rule-based model, or do we train a model on our own data? Should we train a neural network, or will a simple linear model meet our requirements?

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Natural Language Processing (NLP) is a subfield of machine learning concerned with processing and analyzing natural language data, usually in the form of text or audio. Some common challenges within NLP include speech recognition, text generation, and sentiment analysis, while some high-profile products deploying NLP models include Apple's Siri, Amazon's Alexa, and many of the chatbots one might interact with online. To get started with NLP and introduce some of the core concepts in the field, we're going to build a model that tries to predict the sentiment (positive, neutral, or negative) of tweets relating to US Airlines, using the popular Twitter US Airline Sentiment dataset. Code snippets will be included in this post, but for fully reproducible notebooks and scripts, view all of the notebooks and scripts associated with this project on its Comet project page. Let's start by importing some libraries.

We're excited to announce an end-to-end solution that leverages natural language processing to analyze and visualize unstructured text in your Amazon Elasticsearch Service domain with Amazon Comprehend in the AWS Cloud. You can deploy this solution in minutes with an AWS CloudFormation template and visualize your data in a Kibana dashboard. Amazon Elasticsearch Service (Amazon ES) is a fully managed service that delivers Elasticsearch's easy-to-use APIs and real-time capabilities along with the availability, scalability, and security required by production workloads. Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics to extract insights from the content of documents. Customers can now leverage Amazon ES and Amazon Comprehend to index and analyze unstructured text, and deploy a pre-configured Kibana dashboard to visualize extracted entities, key phrases, syntax, and sentiment from their documents.

Sentiment analysis is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. In this article, I will show you how to build your own program to determine if an article on a website is positive, negative, or neutral using the Python programming language. If you prefer not to read this post and would like a video representation of it, you can check out the YouTube Video below and the full code on my Github. It goes through everything in this article with a little more detail and will help make it easy for you to start programming your own article sentiment analysis program even if you don't have the programming language Python installed on your computer. Or you can use both as supplementary materials for learning!

Today's marketers are rightfully obsessed with metrics. But don't forget that your customers are more than just data points. And yeah, it's easy to overlook our customers' feelings and emotions, which can be difficult to quantify. However, consider that emotions are the number one factor in making purchasing decisions. With so many consumers sharing their thoughts and feelings on social media, it quite literally pays for brands to have a pulse on how their products make people feel.

Government agencies face similar challenges when it comes to understanding--and gaining intelligence from-- foreign language content. They need to process, manage and gain insight from large volumes of content locked away in different formats, often across multiple languages. And they need to do all of this as quickly as possible. It's no mean feat when you consider the mindboggling amounts of content being generated: 90% of the world's content was created over the past two years alone. Machine translation and text analytics have always been regarded as the two main ways for organizations and agencies to tackle this challenge.

LinkedIn has begun to offer brand marketers more data to build content. Annalect, the global analytics arm of Omnicom, recently launched a new product, Professional Audiences, leveraging that data to give marketers a better understanding of the content consumed by business audiences using LinkedIn data related to industry, company size, location and title. The details to construct a very detailed audience segment for the B2B space were missing, acknowledges Anna Nicanorova, VP of engineering at Annalect. "LinkedIn provides the professional perspective on audiences," she said, explaining how the technology works. "The hope is to feed the bag of [keyword] tags based on B2B audiences back into Omni and target consumer audiences," Nicanorova said, calling it the company's "bag of words strategy."