p> To download the Restaurant_Reviews.tsv dataset in use, click here . Step 2: Clean up or preprocess the text
# Importing libraries
numpy as np
pandas as pd
# Import dataset
pd.read_csv ( code >
To download the Restaurant_Reviews.tsv dataset in use, click here .
Step 2: Clean up or preprocess the text
| tr > |
Examples: before and after applying the above code yes (reviews = & gt; before, body = & gt; after)
Step 3: For this we need
CountVectorizer class CountVectorizer from sklearn.feature_extraction.text.
We can also set the maximum number of features (the maximum number of features that help the most with the "max_features" attribute). Train on the corpus and then apply the same transformation to the corpus ".fit_transform (corpus)" and then convert it to an array. If the feedback is positive or negative, the answer is in the second column of the dataset [:, 1]: all rows and 1st column (indexed from zero).
For this we need
Description of the dataset to be used:
- Columns seperated by (tab space)
- First column is about reviews of people
- In second column, 0 is for negative review and 1 is for positive review
Step 5: Separation of the body into training and test set. For this we need the train_test_split class from sklearn.cross_validation. The split can be done 70/30 or 80/20 or 85/15 or 75/25, here I choose 75/25 via "test_size".
X — a bag of words, u — 0 or 1 (positive or negative).
Step 6: Selecting a forecasting model (here b random forest)
Step 7: Determining the final results using the .predict () method with the X_test attribute
Note: Accuracy with random forest was 72% (may vary if experimenting with different test size, here = 0.25).
Step 8: You need a confusion matrix to know the accuracy.
Confusion matrix — it is a 2X2 matrix.
TRUE POSITIVE: measures the proportion of actual positives that are correctly identified.
TRUE NEGATIVE: measures the proportion of actual positives that are not correctly identified.
FALSE POSITIVE: measures the proportion of actual negatives that are correctly identified.
FALSE NEGATIVE: measures the proportion of actual negatives that are not correctly identified.
Note. True or false means that the assigned classification is correct or incorrect, and positive or negative refers to the assignment of a positive or negative category.
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