What methods can be used for sentiment analysis?

Sentiment analysis is a crucial aspect of natural language processing that aims to determine the emotional tone behind a series of words. Analyzing sentiment typically involves identifying whether the sentiment conveyed in a piece of text is positive, negative, or neutral.

The correct choice highlights three effective methods used for sentiment analysis:

• Lexicon-based approaches utilize predefined lists of words and their associated sentiment scores to evaluate the sentiment of a text. This method relies on dictionaries that classify words as positive, negative, or neutral, allowing for the calculation of an overall sentiment score based on the occurrence of these words in the text.

• Machine learning involves training algorithms on labeled datasets in which examples of texts are associated with their sentiment. By learning patterns in the data, machine learning models can predict sentiment in new, unseen texts. This method often outperforms simpler approaches because it can capture complex nuances in language.

• Rule-based systems apply a set of hand-crafted rules to identify sentiment. These rules can be based on various linguistic attributes, such as negations or modifiers that affect the sentiment of nearby words. Rule-based systems are beneficial for capturing specific domain-related sentiments.

In contrast, the other options consist of methods that do not directly relate to the core techniques utilized in sentiment analysis. While