Sentimental & Semantic Analysis

Most often, sentimental and semantic analysis are performed on text data to monitor product and brand sentiment in customer chats, call centers, social media posts and more. When a business wants to understand where it stands and what its customers need, this analysis technique delivers results. Alice then focuses on a specific subpopulation to validate the actual error causes related to the presence of the island token. 5 c in the Subpopulation Statistics tab she sees that the size of the subpopulation in the training set (government genre) is extremely small, with just 15 examples containing the word “island”. Also, most of the errors appear when the model predicts neutral, possibly because the model has low confidence about the relationship between hypothesis and premise in this subpopulation. High-level features include a list of pre-defined high-level metrics that are commonly used in error analysis, such as document length [16], part-of-speech tags  [14], and word overlap (for the NLI task) [19].

This leads to making big data more important in several domains such as social networks, internet of things, health care, E-commerce, aviation safety, etc. The use of big data has become increasingly crucial for companies due to the significant evolution of information providers and users on the web. In order to get a good comprehension of big data, we raise questions about how big data and semantic are related to each other and how semantic may help.

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First, you’ll use Tweepy, an easy-to-use Python library for getting tweets mentioning #NFTs using the Twitter API. Then, you will use a sentiment analysis model from the