COMMENT-BASED BEHAVIOR PREDICTION
Negative driver behaviors may be captured based on passenger comments. A set of comments from a set of first users may be obtained. A set of preprocessed words may be generated based on the set of comments. A numerical vector may be generated based on the set of words. A sparse matrix may be generated based on the numerical vector. The sparse matrix may be input into a trained model. A second user may be classified based on an output of the trained model.
1 . A method for classifying users, comprising:
obtaining a set of comments from a set of first users;
generating a set of preprocessed words based on the set of comments;
generating a numerical vector based on the set of words;
generating a sparse matrix based on the numerical vector;
inputting the sparse matrix into a trained model; and
classifying a second user based on an output of the trained model.
2 . The method of claim 1 , wherein the set of comments are obtained through a ride sharing service after a trip.
3 . The method of claim 2 , wherein the set of first users comprise passengers of the ride sharing service; and
wherein the second user comprises a driver of the ride sharing service.
4 . The method of claim 3 , wherein classifying the driver comprises:
classifying the driver as at least one of a safe driver, a dangerous driver, and an abusive driver.
5 . The method of claim 1 , wherein generating the set of preprocessed words comprises:
removing stop words, accents, and special symbols from the set of comments;
determining a set of important words from the set of comments;
correcting typographical errors and standardizing abbreviations in the set of important words; and
replacing similar words in the set of important words with standardized words.
6 . The method of claim 5 , wherein determining the set of important words comprises:
calculating a term frequency-inverse document frequency of each word in the set of comments.
7 . The method of claim 1 , wherein the numerical vector is generated by transforming each word in the set of preprocessed words into a numerical value.
8 . The method of claim 1 , wherein the sparse matrix comprises a set of non-zero values from the numerical vector and a set of indexes of the non-zero values.
9 . The method of claim 1 , wherein the method further comprises:
obtaining a set of tags from the set of first users, wherein the set of tags is associated with at least one comment of the set of comments; and
determining a likelihood of whether each tag of the set of tags is correct based on the classification of the second user.
10 . The method of claim 1 , wherein the method further comprises:
training the trained model based on a set of historical comments associated with a set of historical driver classifications.
11 . The method of claim 1 , wherein training the trained model further comprises:
correcting false negative classifications and false positive classifications in the set of historical driver classifications.
12 . A system for identity and access management, comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors and configured with instructions executable by the one or more processors to cause the system to perform operations comprising:
obtaining a set of comments from a set of first users;
generating a set of preprocessed words based on the set of comments;
generating a numerical vector based on the set of words;
generating a sparse matrix based on the numerical vector;
inputting the sparse matrix into a trained model; and
classifying a second user based on an output of the trained model.
13 . The system of claim 12 , wherein the set of comments are obtained through a ride sharing service after a trip.
14 . The method of claim 13 , wherein the set of first users comprise passengers of the ride sharing service; and
wherein the second user comprises a driver of the ride sharing service.
15 . The method of claim 14 , wherein classifying the driver comprises:
classifying the driver as at least one of a safe driver, a dangerous driver, and an abusive driver.
16 . The method of claim 12 , wherein generating the set of preprocessed words comprises:
removing stop words, accents, and special symbols from the set of comments;
determining a set of important words from the set of comments;
correcting typographical errors and standardizing abbreviations in the set of important words; and
replacing similar words in the set of important words with standardized words.
17 . The method of claim 16 , wherein determining the set of important words comprises:
calculating a term frequency-inverse document frequency of each word in the set of comments.
18 . The method of claim 12 , wherein the sparse matrix comprises a set of non-zero values from the numerical vector and a set of indexes of the non-zero values.
19 . A non-transitory computer-readable storage medium configured with instructions executable by one or more processors to cause the one or more processors to perform operations comprising:
obtaining a set of comments from a set of first users;
generating a set of preprocessed words based on the set of comments;
generating a numerical vector based on the set of words;
generating a sparse matrix based on the numerical vector;
inputting the sparse matrix into a trained model; and
classifying a second user based on an output of the trained model.
20 . The non-transitory computer-readable storage medium of claim 19 , wherein the set of comments are obtained through a ride sharing service after a trip.