need to rewrite a report !

I want you to rewrite a report. You can use any resource that helps you rewrite it. Just write whatever you understand that bases on the report.

Toxic Comments Classification
Yue Xiong

University of California, San Diego

Hongjian Cui

University of California, San Diego

ABSTRACT
This paper analyses a data set of comments from Wikipedia’s talk
page edits and tries to detect different types of toxicity, such as
threats, obscenity, insults, and identity-based hate. It attempts to
do the task by building a Logistic Regression model and comparing
different sets of TF-IDF features to find best one to classify each
toxicity type.

1 INTRODUCTION
Online abusing occurs everywhere. It makes discussing things we
care about online difficult. While we want to chat with others, there
is always somebody insulting people online. The threat of harass-
ment online means that many people stop expressing themselves
and give up on seeking different opinions. Obscene, hateful, and
nasty comments or texts not only lead to verbal violence online, in-
cluding sexual harassment, personal attacks on other organizations
or characters by a well-known organization, denying or slandering
them in a disrespectful and vicious manner, bullying others or even
Racism in hostile languages, and sometimes life-threatening, can
be found on online social platforms such as blogs, hate sites, and
comment sections. Although people are working to improve the
security of the online environment based on the ability to crowd-
source voting plans or condemn comments, in most cases these
technologies are inefficient and cannot predict the potential for
toxicity. Platforms struggle to effectively facilitate conversations,
leading many communities to limit or completely shut down user
comments. There are already lots of researchers working on tools to
help improve online conversation. One area of focus is the study of
negative online behaviors, like toxic comments (comments that are
disrespectful or obscene). While in the past, the toxic comments are
often detected by human, nowadays we can use machine learning
tools to help us.

In this study, we will try to implement a machine learning algo-
rithm and try to classify the toxic comments from normal comments.
What’s more, if a comment is identified as “toxic”, the model we
built will also determine which “toxic” category it belongs to.

The paper is organized as follows: Section 2 tells the information
of dataset and explores the relationship of feature with in the dataset.
Section 3 how we preprocess our data in order to fit it into our
model. Section 4 discusses the models we use in this task. Section 5
discusses the related literature of similar work (Classifying toxic
comments). Section 6 summarize the work and describe the results
and conclusions.

2 DATASET
2.1 Identify a dataset to study
We use a data set from Kaggle that contains 223,549 comments from
Wikipedia talk page edits to perform this study. We choose this data
set mainly because it is well labeled. Each comment (data point)

has been exactly classified as non-toxic or toxic (if it is toxic, it tells
which kind of toxic it belongs to).

We separate the dataset and use 158571 number of comments
as our training set. A validation set (contains 159,571 comments),
is used to evaluate each of our model performs and select the best
one. We will use the test set (contains 57,580 comments) to evaluate
the “best” model’s performance. We will evaluate the performance
by doing cross-validation. A final prediction accuracy is calculated
from performance on the test set.

2.2 Exploratory analysis
2.2.1 Dataset’s feature explanation.

Each data point contains of the following features.
Feature name Explanation
id The id of the comments
comment_text The text of the particular com-

ment
severe_toxic One-hot encoding indicating

whether a comment contains se-
vere toxic information

obscene One-hot encoding indicating
whether a comment contains
obscene information

threat One-hot encoding indicating
whether a comment contains
threat information

insult One-hot encoding indicating
whether a comment contains in-
sult information

identity_hate One-hot encoding indicating
whether a comment contains
identity hate information

2.2.2 Feature exploratory.
We first make a plot and take a look at the general information of

the dataset. Figure 1 shows the distribution of the toxic comments.
We can see that the distribution of the toxic comments is not uni-
form. The number of “severe_toxic”, “threat” and “identity_hate”
comments are extremely less than the others. Also, noted that the
sum of all toxic comments is 15,294, which is far less than total
number of comments (159,571) we can know that there are lots
of non-toxic comments in the training set (the one-hot encoding
corresponds to toxic information of which are all 0).

Furthermore, we look inside the comment and try to dig some
useful information. Since one comment can be classified as dif-
ferent kinds of category at the same time, there might be some
relationship between different types of toxic comments. What we
do is we compute a correlation matrix and visualize it to find out
the correlation between each of the category (Figure 2). The re-
sult shows that “toxic” comments are clearly strongly correlated
with both “obscene” (pearson correlation of the two is 0.68) and

CSE 158, November, 2019, UCSD, California, USA Xiong and Cui, et al.

Figure 1: distribution of toxic comment

“insult” comments (pearson correlation of the two is 0.65). The “in-
sult” comment is also strongly correlated with “obscene” comment”
(pearson correlation of the two is 0.74). To see the overlap between
these toxic categories more clearly, we generate a Venn Diagram
for “toxic”, “insult” and obscene” comments. It proves what we get
from the correlation matrix.

Figure 2: correlation matrix of each type of comment

What we then do is try to look into each category of toxic com-
ments and try to find out the words which are most frequently use
by different types of toxic comments. Below are the word clouds
generated from each category of toxic comments (Figure 3, Fig-
ure 4, Figure 5, Figure 6, Figure 6, Figure 7, Figure 8). From these
graphs, we can actually see that toxic comments of category “toxic”,
“severe_toxic”, and “obscene” actually share very similar words,
while category of “insult” and identity_hate” have very similar
most frequently used words.

Figure 3: wordcloud of comments from identity_hate

Figure 4: wordcloud of comments from insult

Figure 5: wordcloud of comments from obscene

Toxic Comments Classification CSE 158, November, 2019, UCSD, California, USA

Figure 6: wordcloud of comments from toxic

Figure 7: wordcloud of comments from severe_toxic

Figure 8: wordcloud of comments from threat

3 PREDICTIVE TASK
Our predictive task is to determine whether a comment is toxic
or not. If it is toxic, we also need to determine which of the 6

toxic categories it belongs to. In other words, what we will do is
try to predict the one-hot features in the dataset. Note that since
one comments can belong to several category at the same time,
the way we build our model is different than normal. Instead of
building one model that predict the result in one time, we build 6
separate model to predict whether a certain comment belongs to 6
of the certain toxic categories and combines the results together.
The reason we do this is that in this way we can maximize the
accuracy in predicting single toxic category and thus increase the
total accuracy in the end.

The baseline we will use for comparison is a naïve model from
what we have used from Assignment 1 task 2 which is doing “Bag
of Words” approach and use the number of each words’ frequency
as feature matrix and apply logistic regression. The reason that why
the model is appropriate for this task is that from what we have
seen from the word cloud, different categories of toxic comments
tend to have different most frequently used words. So if we count
the how many times each words appear in the comments, it is likely
that we will be able to distinguish the comments. Also, if we dig
this task and Assignment 1 task 2 deeper, we can find that both
tasks try to identify the category of a certain text (comment), so
what works in the past should also have pretty good result in this
task.

In order to improve our performance, we need to preprocess
the data and generate some new features so that it can fit into our
machine learning models.

From the result of our exploratory analysis and the task itself we
know that the key of the task is try to identify the words feature
in the text. What we need to do first is merge different inflections
of words (“stemming”). After the that, we remove the “stopwords”
because we don’t want meaningless words appear in our feature
vector. What we then do to our dataset is basically “Bag of Words”
and TF-IDF approach and try to generate word feature vectors that
represent each comment.

4 MODEL
We use Logistic Regression as our machine learning model. Gen-
erally, we choose this model we learned from class because it got
pretty good result in our assignment 1.

4.1 Baseline

As what we do in Assignment 1 Task 2, after we generate the
feature vector. We try the unigram approach. What we do is count
each word’s frequency and compute the feature vector. Then put it
into the logistic regression model. One of the key issues we faced
in estimating the model is tuning the parameters. In this task, we
use gird search to tune the parameters of logistic regression (the
value of C, which corresponds to the penalty we add to the model
to prevent overfitting) and number of words we want to use as our
feature vector.

Below are the results on validation set.

CSE 158, November, 2019, UCSD, California, USA Xiong and Cui, et al.

category Accuracy
toxic 0.95411
severe_toxic 0.94236
obscene 0.96347
threat 0.94235
insult 0.95753
identity_hate 0.95436

4.2 TF-IDF approach

Because from what we have seen from the word cloud, each type
of toxic comments has many similar most frequently used words.
This might be an issue in the baseline model because we may not be
able to classify two types of comments that have almost the same
10 most frequently used words. This is why we turn to use TF-IDF
approach. Instead of simply counting the number of times each
word appears in comments, TF-IDF approach tries to look deeper.
It increases proportionally to the number of times a word appears
in the document and is offset by the number of documents in the
corpus that contain the word, which helps to adjust for the fact that
some words appear more frequently in general. Using the sklearn
function TfidfVectorizer we can extract “Bag of Words” from the
text and apply TF-IDF weights to each of the words.

After preprocessing the data, we apply unigram approach. It is
similar as the baseline, but now instead of using the number of
times each word appears, we use the computed tfidf value. Again,
we apply grid search to find the best parameter. The accuracy is as
follows

Below are the result on the validation set.
category Accuracy
toxic 0.97256
severe_toxic 0.98273
obscene 0.98633
threat 0.98753
insult 0.97801
identity_hate 0.97512

4.3 Bi-grams

Using unigram may face some problems such as making word
“good” and “not good” having same weights associated in a senti-
ment model. This issue can be fixed by applying bigrams, which is
counting words of two at one time. We apply bigrams method in
our model and same as what we did before, we use grid search to
estimate the best value of parameter. The result is as follows

Below are the result on the validation set.
category Accuracy
toxic 0.97411
severe_toxic 0.97453
obscene 0.98947
threat 0.98983
insult 0.97314
identity_hate 0.97041

As we can severe_toxic and identity_hate type classification
gives best score with unigram features, the rest of the types do best
with bigrams. So now we can apply corresponding vectorizers to
test data and make predictions.

4.4 Problems

One of the issues we met in implementing our approach is that we
always find memory error when we are making the feature vector
and training our model. The reason of which is that our naïve way
of building feature matrix always use loop and didn’t use sparse
matrix, which is very inefficient and memory expensive. We solve
this by trying using the existed sklearn method, “CountVectorizer”,
“TfidfVectorizer”. It not only solve the memory error problem, but
also reduce our time of training one time from several hours to
couple of minutes!

5 RELATED LITERATURE
The dataset we use is from Kaggle (https://www.kaggle.com/c/jigsaw-
toxic-comment-classification-challenge). It was used as a competi-
tion whose purpose is to build a multi-headed model that’s capable
of detecting different types of toxicity like threats, obscenity, insults,
and identity-based hate better than Perspective’s current models
(similar to our task).

There are ongoing experiments to test and calculate the existence
of various toxicity on online platforms, including effective models
established by industry and research groups on micro and macro
blog sites that can detect and predict online toxicity reviews.

Due to the rapid growth of online interactions among users,
this is of great significance in the research field. The rise of the
deep neural system (DNN) has greatly benefited natural language
processing (NLP) due to their high performance and fewer design
highlights. DNN has two basic structures: Convolutional Neural
Network (CNN) (LeCun et al., 1998) and Recurrent Neural System
(RNN) (Elman, 1990)[3]. The input of most NLP tasks is sentences
or documents represented as matrices. Each row of the matrix cor-
responds to a token, which is usually a word, but it can also be a
character. That is, each row is a vector representing a word. Usually,
these vectors are word embeddings (low-dimensional representa-
tions), such as word2vec or GloVe, but they can also be one-stop
vectors for indexing words into a vocabulary[2]. Using the word
embedding technique, the results of the primary neural network
algorithm are also compared with complex convolutional neural
networks and recursive neural networks, and the result is: LSTM
(Long Short-Term Memory) results. The analysis obtained shows
that the performance of LSTM is better than CNN. Given the same
number of epochs, both accuracy and time performance are the
same, so it is preferable to CNNs with word-level embeddings. By
using finer pre-processed data and progress in developing the pro-
posed LSTM system, word embedding can be further improved,
resulting in more accurate and promising results[1].

6 RESULT AND CONCLUSIONS
Below are the result on the test set.

category Accuracy
toxic 0.97334
severe_toxic 0.98789
obscene 0.98486
threat 0.98032
insult 0.97266
identity_hate 0.97752

Toxic Comments Classification CSE 158, November, 2019, UCSD, California, USA

We start our work by building a feature vector that only count
the number of times each word appears. We improve this by using
TF-IDF approach which uses the tf-idf value of the word as feature
matrix. The latter model performs much better than the previous
one. The parameters of the model represent the weight of each
words’ tf-idf value contributes to the result. The reason why TF-IDF
approach is much better than the previous one is due to the fact
that there are many words which have very similar frequency in all
comments, but very different frequency in different types of toxic
comment.

7 FUTURE WORK
As what we mention in the related literature, maybe we should
try to use CNN, RNN approach and try to use word2vec or GloVe
method to compute the feature vector.

REFERENCES
[1] Maeve Duggan. Online harassment. Pew Research Center, 2014.
[2] Hossein Hosseini, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. De-

ceiving google’s perspective api built for detecting toxic comments. arXiv preprint
arXiv:1702.08138, 2017.

[3] Ye Zhang and Byron Wallace. A sensitivity analysis of (and practitioners’ guide
to) convolutional neural networks for sentence classification. arXiv preprint
arXiv:1510.03820, 2015.

• Abstract
• 1 Introduction
• 2 Dataset

2.1 Identify a dataset to study
2.2 Exploratory analysis

• 3 Predictive task
• 4 Model

4.1 Baseline
4.2 TF-IDF approach
4.3 Bi-grams
4.4 Problems

• 5 Related literature
• 6 Result and conclusions
• 7 Future work
• References