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if I use TextBlob in Python to get data label 0/1 of texts (postive/negative) and then use logistic regression for training and prediction, is this feature method or rule based method?

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  • $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    Dec 5, 2021 at 15:29

1 Answer 1

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Sentiment analysis


Sentiment analysis is one of the most widely known Natural Language Processing (NLP) tasks. Sentiment analysis is the task of determining the emotional value of a given expression in natural language.

It is essentially a multiclass text classification text where the given input text is classified into positive, neutral, or negative sentiment. The number of classes can vary according to the nature of the training dataset.

Sentiment analysis generally falls into two categories:

  • Rule-based algorithms
  • Machine Learning models

Rule-Based Algorithms

Rule-based sentiment analysis is one of the very basic approaches to calculating text sentiments. It only requires minimal pre-work and the idea is quite simple, this method does not use any machine learning to figure out the text sentiment. For example, we can figure out the sentiments of a sentence by counting the number of times the user has used the word “bad” in his/her review.

Machine Learning Models

Machine learning models rely on feedback data that has already been assessed by humans to have a particular sentiment. Each piece of feedback is labelled by a human reader who may place the feedback into a particular category. The categories could be as simple as positive, negative, or neutral. As long as there exists labelled data, a machine learning model can often identify complex concepts.

For example, a car manufacturer may desire to classify the sentiment of feedback from past buyers as “budget-conscious”, “eco-conscious”, “tech-enthusiastic”, “luxury-driven”, “performance-driven”, etc. Assuming there is adequately labelled training data for each of these categories, a machine learning model could assign a score for each category. This could help analysts understand the brand better, answering questions about what consumers do or do not like about a particular vehicle.

if I use TextBlob in Python to get data to label 0/1 of texts (positive/negative) and then use logistic regression for training and prediction, is this feature method or rule-based method?

It's ML Method

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