Sentiment Analysis in Scala with Stanford CoreNLP

So far in this series, we have looked at finatra and sbt open-source Scala projects. This week I decided to learn 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.

From the Stanford CoreNLP website,

Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc.

You can read full blog here https://github.com/shekhargulati/52-technologies-in-2016/blob/master/03-stanford-corenlp/README.md

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Day 20: Stanford CoreNLP–Performing Sentiment Analysis of Twitter using Java

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