MTH 522 – 11/17/2023

Opinion mining, or sentiment analysis, is a natural language processing activity that aims to identify the sentiment expressed in a given text. This sentiment can be neutral, positive, negative, or a combination of these. Sentiment analysis has several uses, such as evaluating sentiments in social media content, analyzing consumer feedback, and determining public opinion.
Text Preprocessing: Text data usually goes through preprocessing steps like tokenization, stemming, and stop word removal before being subjected to sentiment analysis. These procedures help to provide a more successful analysis by standardizing and cleaning the text.
Feature extraction: To represent the data required for sentiment analysis, features must be extracted from the preprocessed text. Word embeddings, n-grams, and word frequencies are often used features that serve as a basis for intelligent sentiment analysis.
Sentiment lexicons are collections of words that have been matched with a sentiment polarity, which denotes whether the words are neutral, positive, or negative. In order to provide a more complex comprehension of the sentiment portrayed, these lexicons are essential for matching terms within the text and providing appropriate sentiment scores.

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