Text analytics in NLP is all about processing and analyzing large-scale structured and unstructured text to discover hidden patterns and themes and derive contextual meaning and relationships. Text analytics has so many potential use cases, such as sentiment analysis, topic modeling, TF-IDF, named entity recognition, and event extraction.
Sentiment analysis includes many example use cases, such as analyzing the political opinions of people on Facebook, Twitter, and other social media. Similarly, analyzing the reviews of restaurants on Yelp is also another great example of Sentiment Analysis. NLP frameworks and libraries such as OpenNLP and Stanford NLP are typically used to implement sentiment analysis.
However, for analyzing sentiments using text, particularly unstructured texts, we must find a robust and efficient way of feature engineering...
