What is Sentiment Analysis?

[@aninditachakraborty847]

Martin Sir is a very nice teacher. Thank you for explaining us in a simple manner.

[@bikedawg]

FROM CHATGPT: In sentiment analysis using a machine learning approach, logistic regression is the appropriate classification algorithm, not linear regression. Here's why: *Logistic Regression* Classification Task: Sentiment analysis is typically a binary classification task (positive vs. negative sentiment) or a multi-class classification task (e.g., positive, neutral, negative). Logistic regression is designed for classification problems, making it suitable for sentiment analysis. Output: Logistic regression outputs probabilities that can be mapped to discrete classes (e.g., sentiment labels). The sigmoid function is used to convert the linear combination of input features into a probability between 0 and 1. The model predicts the class label based on the highest probability. Decision Boundary: Logistic regression creates a decision boundary to separate different classes based on the feature space, which is essential for classification tasks. *Linear Regression* Regression Task: Linear regression is used for regression problems, where the goal is to predict a continuous outcome variable. It is not suitable for classification tasks because it predicts a continuous value rather than discrete class labels. Output: Linear regression outputs a continuous value, which is not directly interpretable as class labels for sentiment analysis. Decision Boundary: Linear regression does not create a decision boundary for classification. Instead, it fits a line (or hyperplane) to predict continuous values.

[@BibekMishra84]

I agree. The output of Linear Regression is on continuous spectrum & the output of Logistic regression is discrete (e.g. 0 or 1)

[@ahmadsaud3531]

thanks, you are one of the best guys who can explain complex concepts in an easy and smooth way, i really appreciate your efforts

[@ShoyomboRaphael]

Thank you, IBM. This really breaks it down into digestible bits.

[@toenytv7946]

This was a real information packed thought thumper. Nice job glad I seen the short and your link to it.

[@mememaster69-n4x]

Other Companies: thinking about profit Meanwhile Martin sir and IBM: Lets teach almost each week

[@bikedawg]

These videos are extremely informative and educational--thank you IBM!

[@eudoxieumwali8204]

Nice explanations! Thanks a lot

[@Patricia-y8Patricia_44x0]

Lost in the world of funds - a poetic reflection on the journey to reclaim them.

[@meryemmeryem8434]

Thank you so much 😊

[@snehallande6718]

very well explained

[@seanurquhart3179]

I would sell a Kidney, maybe both, to take courses from Martin on anything computer science, especially AI, related.

[@varunpratapsingh6138]

Excellent 👌🏻 Explanation

[@oshkit]

@IBM Technology please confirm whether it was meant to be logistic regression instead of linear regression

[@sammyfrancisco9966]

One other synonym to lexicon often used by NLP is ontology.

[@sarehsadeghi-j8s]

That was perfect

[@glenilame9910]

how did he not mention transformers? Please make a video on deploying BERT

[@manitahriri9204]

awesome

[@evandrogoulart7307]

Linear regression is not a classification method!

[@serhatakay8351]

He Said linear regression can be used to measure score, based on score sentiment can be classified based on a threshold, e.g. lower score than threshold means negative and vice versa

[@BANOTHPRADEEP-io7rg]

How ontology is used

[@sureshkumar-oq6vt]

Sir 🫡🫡🫡

[@ahmedshaikh3438]

They can read minds. You guys have been illegally reading my mind via A.I. mind reading technology for over a year now.

[@ArghadeepPan-qy5jg]

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