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.
Continue reading “Building a Sentiment Analysis Python Microservice with Flair and Flask”
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. Continue reading “Sentiment Analysis in Scala with Stanford CoreNLP”
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
Today for my 30 day challenge, I decided to take a break from JavaScript and learn about text processing using the Python programming language. I will be focusing on Sentiment Analysis in this blog. My interest in sentiment analysis is few years old when I wanted to write an application which will process a stream of tweets about a movie, and then output the overall sentiment about the movie. Having this information would help me decide if I wanted to watch a particular movie or not. You can read full blog here https://www.openshift.com/blogs/day-9-textblob-finding-sentiments-in-text
Shekhar Gulati 