ChatGPT is a language model developed by OpenAI. It is currently used for generating human-like text based on the input given to it. But it’s important to note that it isn’t designed specifically for sentiment analysis. However, with some tweaking, it could possibly assist in deriving sentiment from text.
Here’s a high level approach to it:
1. Preprocessing: Start with data cleaning where we remove unnecessary noise from the data. Noise can be anything like hashtags, emoticons, urls, etc. It can be done using regular expressions or any other methods.
1. Training: Split the data into training and validation dataset and then train the model using the training dataset.
1. Testing: After training, test the model with the testing dataset to check the accuracy of the sentiment analysis. If the accuracy is not up to the mark, the model is trained again and again until the highest possible accuracy is achieved.
Again, it’s important to note that ChatGPT is not primarily designed for sentiment analysis tasks. If your main goal is sentiment analysis, then there are more specialized sentiment analysis tools and libraries available such as NLTK’s VADER in Python and TextBlob that you can use.
For a custom solution using machine learning, one could use natural language processing libraries such as Natural Language Toolkit (NLTK), Gensim, FastText, etc, trained on labeled sentiment data, to perform sophisticated sentiment analysis.
