In this Sentiment Analysis tutorial, You’ll learn how to use your custom lexicon (for any language other than English) or keywords dictionary to perform simple (slightly naive) sentiment analysis using R’s tidytext package. Note: This isn’t going to provide you the same accuracy as using the language model, but it’s going to get you to the fastest solution (with some accuracy tradeoff). This example deals with Turkish Sentiment Analysis Script.
Sentiment Analysis is one of the most wanted and used NLP techniques. Companies like to see what their customers are talking about - like if there’s a new product launch then what’s the feedback about it. Whereever you’ve got Natural Language - like Social Media, Community Pages, Customer Support - Sentiment Analysis as a technique has found its home there. While the technique itself is highly wanted, Sentiment Analysis is one of the NLP fields that’s far from super-accurate and the reason being is a lot of ways Humans talk.
Sentiment Analysis is one of those things in Machine learning which is still getting improvement with the rise of Deep Learning based NLP solutions. There are many things like Sarcasm, Negations and similar items make Sentiment Analysis a rather tough nut to crack. Deep learning as much as it’s effective, it’s also computationally expensive and if you are ready to trade off between Cost (expense) and Accuracy, then you this is the solution for building a negation-proof Sentiment Analysis solution (in R).