Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. But… How does it work? What are the different approaches? What are its caveats and limitations? How can you use sentiment analysis in your business? Below, you'll find the answers to these questions and everything you need to know about sentiment analysis. No matter if you are an experienced data scientist a coder, a marketer, a product analyst, or if you're just getting started, this comprehensive guide is for you. How Does Sentiment Analysis Work? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media. With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service. Before going into further details, let's first give a definition of opinion. Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people's sentiments, appraisals, and feelings toward a subject or topic. In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.
We are currently in an era of data explosion, where millions of tweets, articles, comments, reviews and the like are being published everyday. Developers are taking advantage of the abundance of data and using things like web scraping to do all kinds of cool things. Sometimes web scraping is not enough; digging deeper and analyzing the data is often needed to unlock the true meaning behind the data and discover valuable insights. On this tutorial we will cover how you can use MonkeyLearn and Scrapy to build a machine learning model that will help you analyze vast amounts of web scraped data in a cost-effective way. We will use Scrapy to extract hotel reviews from TripAdvisor and use those reviews as training samples to create a machine learning model with MonkeyLearn.
Among the major reasons we created our Data is the fact that there just are not a ton of open datasets that are great out there for startups, small businesses, and professors to do on work. We consider that open data should become the newest source that is open; we presume clean, enriched big data that is shared is among the essential elements to initiation that is actual. That is why we were very pleased to learn exactly what the people around at MonkeyLearn are doing with a few of the sets we have shared on our Data for Everyone library. MonkeyLearn is a machine learning program that classifies and pulls information. They let you upload custom training sets and make your personal machine learning algorithm that is suitable for the particular use case.
Recently we walked you through on how to train a sentiment analysis classifier for hotel reviews using Scrapy and MonkeyLearn. This tutorial is a perfect example on how we can combine web scraped data and machine learning for discovering valuable insights about a particular industry. With this model we were able to analyze millions of reviews and understand if guests love or hate different hotels. But besides understanding the sentiment of a review, wouldn't be interesting to understand what particular aspects do the guests love or hate about a particular hotel? This post will cover how you can create a machine learning classifier to understand the different aspects of hotel reviews.
Building a quality machine learning model for text classification can be a challenging process. You need to build a training dataset, test different parameters for your model, fix the confusions, among other things. On this post, we will describe the process on how you can successfully train text classifiers with machine learning using MonkeyLearn. What are the categories or tags that you want to assign to your texts? This is the first question you need to answer when you start working on your text classifier.