Flair delivers state-of-the-art performance in solving NLP problems such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and text classification. It’s an NLP framework built on top of PyTorch.
In this post, I will cover how to build sentiment analysis Microservice with flair and flask framework.
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In this post, we will learn how to use Stanford CoreNLP library for performing sentiment analysis of unstructured text in Scala.
Sentiment analysis or opinion mining is a field that uses natural language processing to analyze sentiments in a given text. It has applications in many domains ranging from marketing to customer service. Few years back, I wrote a simple Java application using Naive Bayes classifier to determine whether people liked a movie or not based on sentiment analysis of tweets about a movie.Read More »
Welcome to the eleventh blog of 52 Technologies in 2016 blog series. If you are following this series then you would have probably noticed that I already wrote week 11 blog on tweet deduplication. I was not happy with the content so I decide to write another blog for week 11.
In week 11, I decided to spend time to learn about text processing using the Python programming language. We will only focus on Sentiment Analysis in this blog. I have written about sentiment analysis multiple times in last few years. We learnt how to do sentiment analysis in Scala using Stanford CoreNLP in week 3 blog. Sentiment analysis gives you the power to mine emotions in text. This can help you build awesome applications that understand human behavior. Few years back, I built an application that helped me decide if I should watch a movie or not by doing sentiment analysis on social media data for a movie. There are many possible applications of Sentiment analysis like understanding customer sentiment for a product by analysis of reviews.
You can read full blog here https://github.com/shekhargulati/52-technologies-in-2016/blob/master/11-textblob/README.md
Today for my 30 day challenge, I decided to learn how to use the Stanford CoreNLP Java API to perform sentiment analysis. A few days ago, I also wrote about how you can do sentiment analysis in Python using TextBlob API. I have developed an application which gives you sentiments in the tweets for a given set of keywords. Let’s look at the application to understand what it does. Read the full blog here https://www.openshift.com/blogs/day-20-stanford-corenlp-performing-sentiment-analysis-of-twitter-using-java