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- What is sentiment analysis in Python?
- Which Python library is best for sentiment analysis?
- Which algorithm is best for sentiment analysis?
- Is sentiment analysis AI or ML?
What is sentiment analysis in Python?
Sentiment Analysis is a use case of Natural Language Processing (NLP) and comes under the category of text classification. To put it simply, Sentiment Analysis involves classifying a text into various sentiments, such as positive or negative, Happy, Sad or Neutral, etc.
Which Python library is best for sentiment analysis?
Topping our list of best Python libraries for sentiment analysis is Pattern, which is a multipurpose Python library that can handle NLP, data mining, network analysis, machine learning, and visualization. Pattern provides a wide range of features, including finding superlatives and comparatives.
Which algorithm is best for sentiment analysis?
Sentiment analysis models
Logistic regression is a good model because it trains quickly even on large datasets and provides very robust results. Other good model choices include SVMs, Random Forests, and Naive Bayes.
Is sentiment analysis AI or ML?
It uses machine learning (ML), natural language processing (NLP), data mining, and artificial intelligence (AI) techniques to mine, extract and categorize users' opinions on a company, product, person, service, event, or idea for various sentiments.
