Feature-level Approach for the Evaluation of Text Classiﬁcation Models

Vanessa Bracamonte, Seira Hidano, Toru Nakamura and Shinsaku Kiyomoto

KDDI Research, Inc., Saitama, Japan

Keywords:

Feature Importance Visualization, Text Classiﬁcation Models, Model Evaluation, Case Studies.

Abstract:

Visualization of explanations of text classiﬁcation models is important for their evaluation. The evaluation of

these models is mostly based on visualization techniques that apply to a datapoint level. Although a feature-

level evaluation is possible with current visualization libraries, existing approaches do not yet implement ways

for an evaluator to visualize how a text classiﬁcation model behaves for features of interest for the whole data

or a subset of it. In this paper, we describe and evaluate a simple feature-level approach that leverages existing

interpretability methods and visualization techniques to provide evaluators information on the importance

of speciﬁc features in the behavior of a text classiﬁcation model. We conduct case studies of two types of

text classiﬁcation models: a movie review sentiment classiﬁcation model and a comment toxicity model.

The results show that a feature-level explanation visualization approach can help identify problems with the

models.

1 INTRODUCTION

The use of machine learning models for text classi-

ﬁcation has increased in recent years. Text classiﬁ-

cation models can be used to predict the sentiment

of movie reviews and the toxicity of user comments,

for example. These models can show high accuracy

when they are evaluated, but they can nevertheless

have speciﬁc problems. In one real life example, a

model for predicting the toxicity of user comments

(Jigsaw, 2018) assigned a higher toxicity score to

neutral comments when they included words such as

“black,” “woman” or “gay” (Jessamyn, 2017). The

problem was discovered by an independent evaluator,

and it led to efforts to improve these models (Dixon

et al., 2018).

Different types of users need to evaluate the per-

formance of a model, and a number of tools have been

developed to address this need. There are evaluation

tools that can work with multiple types of models and

data (Zhang et al., 2019; Wexler et al., 2020). Spe-

ciﬁc tools for the evaluation of NLP models and text

data have also been developed (Tenney et al., 2020;

Hoover et al., 2020). The visualization techniques

used in these tools allow the evaluator to locate and in-

spect individual datapoints, and to review the predic-

tion results of a model. NLP model evaluation tools

such as LIT (Tenney et al., 2020) can also leverage

model interpretability methods (e.g local gradients (Li

et al., 2016) and LIME (Ribeiro et al., 2016)) to pro-

vide visualization of the importance of features to the

prediction of a datapoint.

The objective of these visualization approaches is

to provide information to the evaluator that will help

them decide whether the model is performing well or

if it has problems. However, evaluation of individ-

ual datapoints or instances and their prediction out-

comes provides only a partial view of the behavior

of the model. In particular for text data, identifying

if there are patterns of problems related to speciﬁc

features becomes difﬁcult with datapoint-level eval-

uation approaches. In models for image data, similar

features in a group of individual datapoints may re-

veal a pattern (Liu et al., 2019; Chen et al., 2021). For

example, if incorrect predictions of an image classiﬁ-

cation model share a predominant color, then it may

indicate that the model behaves incorrectly for images

of that color. On the other hand, the complexity of

dealing with text data is such that there are not only a

great number of features, but that these features have

semantic meaning, meaning in context and relation-

ships within text. For text, these complexities have to

be taken into consideration (Rohrdantz et al., 2012;

Stoffel et al., 2015). Therefore, patterns such as the

ones identiﬁed in groups of images cannot be easily

identiﬁed from groups of text datapoints. It would

be difﬁcult for an evaluator to know whether a model

behavior problem with a feature identiﬁed in one dat-

apoint exists as a general pattern for the model.

164

Bracamonte, V., Hidano, S., Nakamura, T. and Kiyomoto, S.

Feature-level Approach for the Evaluation of Text Classiﬁcation Models.

DOI: 10.5220/0010846900003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 3: IVAPP, pages

164-170

ISBN: 978-989-758-555-5; ISSN: 2184-4321

A complementary approach, that allows an evalu-

ator to center the exploration on features of interest,

could provide additional information to identify such

patterns. We deﬁne this feature-level approach as one

that centers on features as opposed to datapoints, but

also considers the context of those features. In this pa-

per, we describe and evaluate such an approach. We

developed a prototype for testing the approach and

conducted two case studies: an evaluation of a movie

review sentiment classiﬁcation model, and a compar-

ison of two toxic comment classiﬁcation models. The

results indicate that a feature-level visualization ap-

proach could be a useful complementary approach to

help identify problems in text classiﬁcation models.

2 RELATED WORK

Current tools and libraries for machine learning mod-

els exploration and evaluation make use of a num-

ber of datapoint-level (also known as instance-level

or instance-centric) visualization approaches (Yuan

et al., 2021). This includes, for example, visualiza-

tion of individual datapoints (image, tabular or text

data), features in the datapoint and prediction results.

It also includes model performance metrics visualiza-

tion such as confusion matrices and plotting of predic-

tion results. There are a number of existing tools for

exploring machine learning models that implement

this approach. For text data, one example is the What-

If tool (Wexler et al., 2020), a model evaluation tool

that can be used for exploring different types of mod-

els and data. It provides functions to apply these visu-

alizations to a subset of the evaluation data, through

ﬁltering by labels such as the output and by features,

and the results are visualized at the datapoint level.

Techniques for visualizing explanations of predic-

tions of models for text are also applied at the dat-

apoint level. LIT (Tenney et al., 2020) is an explo-

ration tool speciﬁcally for NLP models. The tool pro-

vides some functions to search for words in a text,

but the visualization of results is provided at the dat-

apoint level. It provides salience map visualization

functions to represent feature importance in a data-

point, calculated using interpretability methods such

as LIME (Ribeiro et al., 2016) and local gradients

(Li et al., 2016). Salience maps are one of the most

frequently used visualization for the results of inter-

pretability methods. In the application of this visual-

ization technique for text, the features of a datapoint

are highlighted in different opacity to indicate the im-

portance of features for the predicted result, and/or

different colors to indicate the prediction class. This

type of visualization is included in most interpretabil-

ity method libraries that handle text data models such

as LIME (Ribeiro et al., 2016), SHAP (Lundberg and

Lee, 2017) and Interpret-Text (InterpretML, 2021). In

addition to the salience map, the most important fea-

tures in a datapoint are sometimes visualized as a bar

chart (Ribeiro et al., 2016), or a force plot (Lundberg

and Lee, 2017).

The type of data affects the usefulness of the data-

point level visualization for ﬁnding patterns of error

in the model which are related to features. In im-

age data, the information can be processed by compo-

nents (Biederman, 1987), and the evaluator can iden-

tify, for example, the subjects and objects in the im-

age, their characteristics, or the predominant colors

of the image. This characteristic of image data has

been useful to identify, for example, if the errors of a

machine learning model are biased towards people of

color (Barraza et al., 2019): if the majority of erro-

neously classiﬁed photos in an image classiﬁer corre-

sponded to dark skin subjects, then visualizing the im-

ages in a group would reveal this pattern. A number

of tools make take advantage of image pattern iden-

tiﬁcation. LabelInspect (Liu et al., 2019) provides a

function to visualize images where a model prediction

was different from the assigned label, and the evalua-

tor could distinguish a problem at a glance. OoDAna-

lyzer (Chen et al., 2021) identiﬁes out-of-distribution

samples and provides a visualization of the group of

images. In the case study evaluation, an evaluator

could identify a ”light-colored dogs and dark-colored

cats” pattern in the misclassiﬁcations.

It may be easy for an evaluator to gather some

idea of potential problems from glancing at an im-

age or a group of images. However, an equivalent

task would be much more difﬁcult in the case of text,

where patterns cannot be distinguished at a glance.

For the same reason, side-by-side comparison visual-

ization of two or more prediction explanations may be

more effective for image data. It would be difﬁcult for

the evaluator to ﬁnd differences at a glance with this

type of visualization for text, in particular for longer

documents.

There are tools that center the model exploration

on features or provide functions for feature-level ex-

ploration in addition to datapoint-level. For example,

Prospector (Krause et al., 2016) allows interactive di-

agnostic of the impact of features on predictions, for

tabular data. There are also proposals for feature-

level visualization for text data. The Text Classiﬁ-

cation Analysis Process proposes examining feature

rankings for text classiﬁcation with the use of word

clouds (Stoffel et al., 2015). FeatureInsight (Brooks

et al., 2015) focuses the visualization on features from

misclassiﬁed texts to support the identiﬁcation of new

Feature-level Approach for the Evaluation of Text Classiﬁcation Models

165

feature. The tool uses a list style of visualization

to reduce cognitive load, but this approach loses the

context of the feature. The approach by the Man-

ifold tool (Zhang et al., 2019) is the closest to our

proposal. The tool provides functions for visualizing

feature distributions and feature-wise comparison for

text data. However, it does not provide visualization

of explanations from interpretability methods. In ad-

dition, feature lists are created based on frequency but

it does not provide functions to ﬂexibly explore fea-

tures that are not in the top list.

3 FEATURE-LEVEL

VISUALIZATION APPROACH

Feature-level visualization of explanations could pro-

vide the evaluator of a text classiﬁcation model with

information that would help them understand if there

are patterns in the errors of a model which are related

to a feature of interest. For this purpose, the evaluator

would need (1) to ﬁnd features of interest in the data,

(2) to know the context of each occurrence of that fea-

ture, and (3) to inspect how the model behaves with

respect to that feature.

We illustrate these needs with an example. An

evaluator might be interested in knowing if a model

that predicts the toxicity of a user comment is pre-

dicting a higher toxicity score when the text con-

tains words related to race, gender or disabilities (Jes-

samyn, 2017). The evaluator would then need to

know the context of a feature when the feature (word)

has multiple meanings. The process requires the eval-

uator to review the text in order to distinguish the

meaning of a word. For example, an evaluator may be

interested in instances where the word “black” is used

to refer to a person, but they may not be interested

in instances where the word has a different meaning,

such as for example in the phrase “black humor.”

Feature-level visualization could be a way to com-

plement current evaluation approaches to address the

needs described. The approach we propose is simple,

and relies on a combination of existing techniques for

text visualization.

Top Features in the Data or in a Subset of the Data.

To visualize the most important features in a subset

or the whole data. Unlike the list top features in in-

dividual datapoints that is provided in the visualiza-

tion of the result of interpretability methods such as

LIME (Ribeiro et al., 2016), a dataset-wide list could

provide the evaluator with a better perspective of the

model behavior.

Feature Importance Plots. One way to visualize the

importance of a feature of interest in multiple data-

points could be through scatter plots. This type of plot

could work to show the importance score of a feature

of interest in the whole dataset, or in a subset of it.

Feature-wise Comparison. Feature importance plots

could be paired with a comparison function, that

would allow to visualize the importance of multiple

features of interest in the data. This type of function

would allow the evaluator to compare, for example,

how a model behaves for the words “woman” and

“man” (or any other contrastive pair of features), in

the texts that contain these words.

Keyword-in-Context Visualization. Information re-

trieval techniques such as concordance and keyword-

in-context (KWIC) visualization (Fischer, 1966) are

speciﬁcally designed for the purpose of ﬁnding and

visualizing text in context. These techniques can help

the evaluator review the context of the feature of in-

terest.

4 EVALUATION

In this section, we present the results of two case stud-

ies used to evaluate the proposed feature-level visual-

ization approach. We ﬁrst describe the prototype de-

veloped for conducting the case study evaluations.

4.1 Prototype

We developed a prototype to test the proposed ap-

proach. We implemented the following feature-level

functions: a feature search with ﬁlters, top features in

the search results, KWIC data list to view the search

results and a feature importance scatter plot with com-

parison options. The data inputs are (a) a labeled

dataset of text data (evaluation data), (b) the predic-

tion results from the model being evaluated (model re-

sults), (c) all features of the dataset, and (d) the impor-

tance scores corresponding to those features, which

are a result of the interpretability method used. The

data was pre-process as follows: the evaluation data

was tokenized to obtain the features that would be the

input to the text classiﬁcation model. A text classi-

ﬁcation model was used to obtain prediction results

with the tokenized data (features) as input. Similarly,

the selected interpretability method was used to gen-

erate explanations (importance scores for each fea-

ture) of the text classiﬁcation model predictions, us-

ing the same tokenized data. The detail of the data,

model(s) and interpretability method used in the eval-

uation is described in each case study. Figure 1 shows

the overview of the prototype.

The scatter plot was complemented by summary

information of the feature importance scores. We

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Tes t data

(Text)

Features

Importance

scores

Prediction

results

ML model

Interpretability

method

Input Data Prototype

Top explanation

features

🔍

KWIC List

Feature importance

scatter plot

Figure 1: Prototype overview.

also implemented visualization of model performance

metrics, salience in the form of text highlights and

comparison between models.

The required data for the case studies was pro-

cessed beforehand: (a) the labeled dataset of text data

(evaluation data), (b) model prediction results, (c) fea-

tures and their importance scores. The approach is

agnostic with regards to how the feature importance

scores are calculated. Therefore, they can be calcu-

lated in different ways, for example with post hoc in-

terpretability methods. For the case studies, we calcu-

lated SHAP importance scores, using the method by

Chen et al. (Chen et al., 2019).

4.2 Movie Review Sentiment

Classiﬁcation Case Study

In this case study, we evaluated a sentiment classiﬁ-

cation model. The objective of the evaluation was to

identify whether there is bias in the model, in partic-

ular related to gender.

4.2.1 Setup

For this case study, we trained a CNN model to pre-

dict the sentiment of a movie review. The model was

a CNN Keras model trained and tested on the Large

Movie Review dataset (Maas et al., 2011). The CNN

model had an accuracy of 88.9%. We did not ﬁne-

tune the model or develop it to be intentionally “bad”

in any particular sense. We also did not know if the

models had problems beyond what we could gather

from the accuracy metrics. Since the focus was gen-

der bias, we used a subset of movie reviews that con-

tained gender-related words as the evaluation data.

4.2.2 Model Evaluation

We found different problems with the model with re-

spect to gender, but here we focus on one case. The

model accuracy for the evaluation data was 93.98%,

but the word “women” appeared as a top negative fea-

ture in the overview (Figure 3). We searched for the

word women to inspect its behavior in the model. The

feature importance scatter plot showed that the ma-

jority of occurrences of this feature contributed to a

negative classiﬁcation.

Figure 4 shows the plot, the importance score of

the word women is indicated in orange. We then

compared the word women with the word men, to

investigate if there were any differences. The plots

showed a difference in how the model handled the dif-

ferent gender-related features, in particular when we

included only false negative results (Figure 4).

In addition, the KWIC result list showed that the

context for the use of the word women was not always

negative for this subset of the data (Figure 2).

From these results we observe that the model

could exhibit problems when the review includes the

word women. We conducted a simple test of this re-

sult by replacing the word in a short text (”It was inter-

esting to see the people in the movie react to the ongo-

ing crisis”). When the word people was replaced with

the word women, the output of the model changed

from positive to negative.

4.3 Comment Toxicity Classiﬁcation

Case Study

We evaluated two models used to categorize the level

of toxicity in social media comments, focusing on

words related to identity.

4.3.1 Setup

The models we evaluated were pre-trained Keras

models developed by ConversationAI (Conversation

AI, 2020) as part of a project to help identify unin-

tended bias in text classiﬁcation models. The differ-

ence between the models is that one has been trained

with unbiased data based on race, gender and other

identities; we use the names original and debiased to

refer to these models. The details of how the models

were trained and on which data are found in (Conver-

sation AI, 2020). We used a subset of comments in

the Wikipedia Talk Labels: Toxicity Dataset (Thain

et al., 2017), which consists of user comments from

Wikipedia (English version, labeled as toxic or non-

toxic, as the evaluation data. The subset contained

words related to identities.

4.3.2 Model Evaluation

We started by exploring the model results on differ-

ent ﬁlter conditions. Filtering out low character count

comments resulted in the word gay appearing as a top

toxic feature. We searched for that word to view how

Feature-level Approach for the Evaluation of Text Classiﬁcation Models

167

Figure 2: Movie review sentiment classiﬁcation case study: Concordance list view of results that include the word “women”.

Figure 3: Movie review sentiment classiﬁcation case study:

Model performance and top explanation features for the

dataset. Accuracy is high, but the word “women” appears

as a top negative feature.

the two models handled it. The overview of the search

results (Figure 5) showed that the word appeared as a

top feature for both models and that the models had a

similar performance for that subset of data.

On the other hand, the feature importance scatter

plot showed that the scores were more dispersed in

the original model compared to the debiased model.

For the original model, the plot showed that the word

gay contributes more often to a toxic score than in

the debiased model, where the scores are closer to the

neutral point. Figure 6 shows this result, where the

toxic scores are indicated in blue. This result visual-

izes that the model on the right (debiased, Figure 6)

was debiased on the word gay compared to the origi-

nal toxicity model.

Figure 4: Movie review sentiment classiﬁcation case study:

Plot of importance scores for “women” compared to “men”

in false negative prediction results.

4.4 Limitations

As mentioned before, a feature-level visualization

approach to model evaluation would necessarily be

complementary to other techniques, and not meant to

be the only approach. In addition, there are the fol-

lowing limitations. First, to be able to visualize any

feature in all datapoints the implementation requires

that the explanations are generated at the initializa-

tion point or beforehand. Second, as with most visu-

alization techniques for text classiﬁcation, we focused

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168

Figure 5: Toxicity models case study: Features for the original (left) and debiased (right) toxicity models for a subset of the

data ﬁltered by character length. The word “gay” appears as a top toxic feature in the original model.

Figure 6: Toxicity models case study: Plot of importance scores for “gay” compared in the original toxicity model (left) vs.

the debiased model (right). The scores are less dispersed in the debiased model.

only binary classiﬁcation results. Third, different in-

terpretability methods could result in different out-

comes so the results of the case studies could differ.

However, the approach could also be used to compare

between the results of interpretability methods for a

particular feature. Finally, we rely on a combination

established visualization techniques for text and on

existing model interpretability libraries. Future work

should consider new visualization techniques.

5 CONCLUSION

Visualization approaches for the exploration and eval-

uation of text classiﬁcation models and their explana-

tions focus on presenting information about individ-

ual datapoints. In this paper, we propose that a simple

feature-level visualization approach could help evalu-

ators understand how the model behaves for features

of interest. Speciﬁcally, we describe an approach that

leverages existing techniques to visualize the most

important features in the data, the overall importance

of a feature in the model and the context of that fea-

ture in the text. We built a prototype to test this ap-

proach, and used it in two case study evaluations.

The results showed that the feature-level approach

can help identify problems in text classiﬁcation mod-

els related to speciﬁc features. Future work should

consider evaluating whether there are other feature-

level visualizations techniques that could be applied

to complement existing evaluation approaches for text

classiﬁcation models.

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169

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