Customized sentiment analysis

As mentioned earlier, sentiment analysis is the process of identifying and extracting sentiment information related to a specified topic, domain, or entity, from a set of documents. The sentiment is identified using trained sentiment classifiers. Thus, the quality and the type of the training data have a big impact on the classifier's performance. Most pre-trained classifiers (like VADER) are trained on general texts because they are designed to be versatile for use on different topics. Unfortunately, when we need to extract sentiment from a specific textual data (for example, very domain specific) such as a general classifier might not perform very well. That is why, it makes great sense to train our own classifier that will fit specific needs, or alternately, just train a general classifier, but based on customized, verified, and known datasets. In short, the magnitude of adaptation to the domain is what makes the difference between a good sentiment analysis...