Can logistic regression be used for sentiment analysis?

Can logistic regression be used for sentiment analysis?

For example: If you had the tweet “I am learning sentiment analysis”, then you would put a 1 in the corresponding index for any word in the tweet, and a 0 otherwise. As we can see, as V gets larger, the vector becomes more sparse.

How can logistic regression improve accuracy?

One of the way to improve accuracy for logistic regression models is by optimising the prediction probability cutoff scores generated by your logit model. The InformationValue package provides a way to determine the optimal cutoff score that is specific to your business problem.

What is the accuracy of the logistic regression classifier?

There are two main metrics for evaluating how well our model functions after we’ve trained it: Accuracy. Represents the percentage of correctly classified samples. An accuracy score of 90% would tell us that our logistic regression model correctly classified 90% of all examples.

How do you improve logistic regression?

1 Answer

1. Feature Scaling and/or Normalization – Check the scales of your gre and gpa features.
2. Class Imbalance – Look for class imbalance in your data.
3. Optimize other scores – You can optimize on other metrics also such as Log Loss and F1-Score.

What is the best measure of predictive ability for a logistic regression?

coefficient of determination
For an ordinary least-squares regression model, the coefficient of determination (R2) describes the proportion (or percentage) of variance of the response variable explained by the model, and is a widely accepted summary measure of predictive power.

How does logistic regression work as a classifier?

Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for given set of features(or inputs), X. Contrary to popular belief, logistic regression IS a regression model.

How to improve sentiment classification with logistic regression?

Let’s recap. We built a sentiment classification model using logistic regression and tried out different strategies to improve upon the simple model. Among those ideas, including bigrams as features has the most improvement in F1 score. For both the simple model and the improved model, we also analyzed its most important textual features.

What do you need to know about logistic regression?

Contrary to popular belief, logistic regression IS a regression model. The model builds a regression model to predict the probability that a given data entry belongs to the category numbered as “1”. Just like Linear regression assumes that the data follows a linear function, Logistic regression models the data using the sigmoid function.

How is sparse representation used in logistic regression?

With the sparse representation, a logistic regression model would have to learn n + 1 parameters, where n would be equal to the size of your vocabulary V. For large vocabulary sizes, this would make your model inefficient in terms of training and inference/prediction/test time.

Why is linearity a limitation in logistic regression?

This is because the scale of measurement is continuous (logistic regression only works when the dependent or outcome variable is dichotomous). Logistic regression assumes linearity between the predicted (dependent) variable and the predictor (independent) variables. Why is this a limitation?