Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

In the expression named entity, the word named restricts the task to those entities for which one or many strings, such as words or phrases, stands (fairly) consistently for some referent. For instance, the automotive company created by Henry Ford in 1903 can be referred to as Ford or Ford Motor Company, although "Ford" can refer to many other entities as well. Wikipedia

Libraries Compared: TextBlob and SpaCy

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. Wikipedia

Libraries Compared: TextBlob and VADER

Text summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content.

There are two general approaches to automatic summarization: extraction and abstraction.

Extraction-based summarization

Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document.

Abstraction-based summarization

Abstractive methods build an internal semantic representation of the original content, and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by paraphrasing sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both natural language processing and often a deep understanding of the domain of the original text in cases where the original document relates to a special field of knowledge. Wikipedia

Libraries Compared: SpaCy and Sumy