Sentiment Analysis of Product Reviews on E-Commerce Platforms
Based on NLP: Take HUAWEI P40 as an Example
Rui Yang, Yuzhu Xie, Jie Sun, Ling Ding, Zhemiao Zhang and Jianjun Wang*
School of Sciences, Sichuan Agricultural University, Ya’an, Sichuan,625014, China
Keywords: Natural Language Processing, HUAWEI, Sentiment Analysis, Online Shopping Review.
Abstract: As the boom and popularity of e-commerce greatly influenced people's consumption patterns and
socio-economic structures, the study of product reviews has great significance for guiding purchases. Most
of these product reviews are subjective and non-structural. Natural Language Processing (NLP) is an
effective method to address communication between humans and computers. It is commonly used to convert
unstructured language into structured language for computer recognition and processing to solve problems
that are difficult to solve manually. In this paper, we put out the previous HUAWEI Mall reviews about
HUAWEI P40 model mobile phone as the data source for natural language processing. And then, we use a
CNN model based on the language of Python to perform sentiment analysis of consumers' attitudes about
this phone. The results illustrate that consumers' concerns about the HUAWEI P40 phone are generally
divided into the following categories: photography, battery life, communication, running speed, display
effect and appearance, and their attitudes are generally positive and optimistic. In other word, the negative
sentiment towards this phone is not significant.
1 INTRODUCTION
1.1 Research Significance
In recent years, in the background of "big data" era,
with the popularity of online shopping, Taobao,
Jingdong and other major e-commerce platforms are
flourishing and competing fiercely (
TAO, ZHANG,
SHI, WEI 2020, ZENG, YU 2020, Feng 2019, Li
2019
). They are not only a platform for selling goods,
but also a platform for consumers to exchange
product use experience as well as quality evaluation.
Therefore, many online users often choose to check
the information and evaluation of a certain product
before buying or using it, and different consumers
have different performance tendencies for the same
type of products.
In order to improve the quality of customer
service, in addition to price wars, it is increasingly
important to understand the needs of customers and
listen to their voices, an important way is data
mining and sentiment analysis of consumers' text
reviews (
TAO, ZHANG, SHI, WEI 2020
). Getting
these information also can improve the
competitiveness of the corresponding products, and
can be used as a guide for consumers' purchases,
thus it is of great practical significance to help
consumers' choices become more rational.
1.2 Current Research Status at Home
and Abroad
Text sentiment analysis is a multidisciplinary
research field, which belongs to the scope of
computer linguistics. It has a wide range of
applications in computer science as well as
management, political science, economics,
sociology, and the booming of business in the
industry has further expanded its applications (
TAO,
ZHANG, SHI, WEI 2020
). Already in the 21st
century, as a new research direction, this field has
gradually become active and attracted increasingly
more researchers. In the application of sentiment
analysis, "user comment analysis and decision
making" is one of the most frequently used
applications of sentiment analysis technology (
Feng
2019
). Many domestic and foreign research
institutions have developed sentiment analysis
systems in various fields according to the specific
needs in real life to help users analyze and make
decisions on large amount of information. Many
802
Yang, R., Xie, Y., Sun, J., Ding, L., Zhang, Z. and Wang, J.
Sentiment Analysis of Product Reviews on E-Commerce Platforms Based on NLP: Take HUAWEI P40 as an Example.
DOI: 10.5220/0011768000003607
In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 802-806
ISBN: 978-989-758-620-0
Copyright
c
2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
commercial systems and applications based on
sentiment analysis have emerged. For example,
Google Shopping, a representative platform based
on product reviews, provides users with product
search and price comparison services for online
purchasing platforms. OpinionE Q allows
commercial organizations and individuals to
customize product analytics services on demand (
Li
2019
). However, due to the increasingly swelling
problem of the diversity of sentiment research
objects and the complexity of sentiment tasks.
Sentiment objects range from positive and negative
tendency comments on products and services to
categorizing user and topic sentiments in social
media categories, with more diverse expressions and
a greater variety of emotions, and a corresponding
shift in research content, including more attention to
the user's information as well as the change of the
emotion for the user (
Li 2019, Ziang, Wang, Li,
Lévy, Nie, Jurafsky, Ng 2017, Lan, Chen,
Goodman, Gimpel, Sharma, ALBERT 2020,
Young, Hazarika, Poria, Cambria 2018, Rush
2018, Vaswani, Shazeer, Parmar, Uszkoreit, Jones,
Gomez, Kaiser, Polosukhin 2017
). On the other
hand, consumers' comments on short texts are
diverse in their expression of emotions, either in a
straightforward manner or through rhetorical devices
or even irony. The multiple forms of expression of
emotions such as irony make the analysis inaccurate.
This project applies a CNN model with fewer
parameters but better performance to text data for
sentiment analysis, and uses the analysis results to
evaluate the attributes of products. This will help
merchants to improve their products and consumers
to choose more suitable products for themselves.
2 METHODS
2.1 Data Collection and Pre-processing
At present, online shopping had become one of the
important ways for people to trade and consume. By
the end of 2020, Taobao and Jingdong two major
e-commerce platforms had reached 1.2 billion active
purchasing users in the past year. Due to consumer
preference, digital enthusiasts tended to purchase
electronic products from official channels. As of
July 2021, as the P40 model phone's main and
official sales channel, more than 30,000 after-sales
reviews had been accumulated in the HUAWEI
Mall. If the entire model phone's review data were
crawled and studied, the process would be very
tedious, resulting in unnecessary human, financial,
and material waste. Therefore, this paper randomly
selected 500 real reviews of HUAWEI P40 model
mobile phones as the basis of the research. The
study first selected 10,000 reviews data of other
mobile phone products with known labels to train
the CNN model. The consumers' attitudes of
"positive", "negative" or "other" are determined
based on the content of the reviews of different
labels.
For text data, text data usually contained a lot of
noise that is not relevant to the task, so it should be
Pre-processed such as removing URL links,
removing emoji emoji, converting English upper
case to lower case and converting Chinese
traditional to simplified, etc.
Simultaneously, 492 valid questionnaires were
collected from online questionnaire survey of
HUAWEI mobile phone users. The results showed
that the users' concern about HUAWEI p40 mobile
phone are mainly focused on six aspects, such as
running speed, photography, battery life,
communication, appearance and display effect.
According to the above six tags, the dataset is
classified by Python in six labels to get six review
sets.
2.2 CNN Model Building
In the field of computer deep learning, CNN
convolutional neural network is one of the most
successful application of deep learning algorithm.
Consisting of neurons with learnable weights and
bias constants, each neuron receives some input and
does some dot product computation to output a score
for each classification.
Input OutputConv
Pool
Conv Pool
Figure 1: CNN convolutional neural network model.
Sentiment Analysis of Product Reviews on E-Commerce Platforms Based on NLP: Take HUAWEI P40 as an Example
803
Score
Display Effect
Appearance
Other
Running Speed
Communication
Battery life
Photograph
Figure 2: HUAWEI Mall HUAWEI P40 review sentiment analysis values (mean).
2.3 Model Training and Data
Processing
Due to the non-full connectivity and weight sharing
characteristics of CNN neural network, which makes
its complexity lower and the number of weights less,
so CNN convolutional neural network is chosen for
the review sentiment analysis of HUAWEI P40
mobile phone. The text in the review set was
Tokenized, and a vocabulary table was built. Then
the Token sequence was converted into a word
embedding matrix. The word embedding was then
fed into the CNN neural network for training. When
training the model, the number of traversals could
control the training time of the model. The smaller
the number of traversals, the shorter the training
time, but the training effect would also decrease.
The learning rate could be adjusted between 0.001
and 0.0001, and different learning rate models
would also have different training effects. After
multiple adjustments, we found that setting the
number of traversals to 30 and a learning rate of
0.001 has the best training effect. At the end of the
final training, the accuracy of the CNN model was
94%, and the model was considered good and the
next step of data processing could be performed.
The 500 data exported from HUAWEI Mall were
imported into the trained model for sentiment
analysis, and the following results were obtained
3 ANALYSIS OF THE RESULTS
3.1 The Overall Attitude of Consumers
Towards Each Label Shows a
Positive Trend
The sentiment analysis of HUAWEI P40 reviews in
HUAWEI Mall showed that the results were divided
into positive and negative scores, representing
whether consumers had a positive or negative
attitude towards the phone. Both positive and
negative scores ranged from 0 to 1, when the score
was greater than 0.5, it was considered that
consumers have an optimistic and positive attitude
towards this feature of the phone. when the score
was less than 0.5, consumers had an unfavorable
attitude. Meanwhile, the closer the value was to 0 or
1, the more absolute consumer attitude was.
The results of the sentiment analysis showed that
the sentiment scores about the HUAWEI P40 phone
concerned by online consumers were greater than
0.5 in all 6 aspects, which means that consumers
were more positive about this phone and generally
ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology
804
Table 1: Evaluation indicators Number of choices Percentage (492 surveys).
Evaluation index Select numbe
r
Proportion
Apearance 457 0.9289
Runnin
g
S
p
ee
d
442 0.8984
Photograph 376 0.7642
Batter
y
life 257 0.5224
HongMeng system 141 0.2866
Communication 137 0.2785
Cost performance 122 0.2480
Effect of dis
p
la
y
105 0.2134
Hand feeling 97 0.1972
Others 70 0.1423
satisfied with it.
Among them, consumers think the best was the
"display effect" of HUAWEI P40, with a value of
0.999 and an image display of 1. Secondly, people
were generally satisfied with the "appearance" and
"running speed" of this phone, with a value of
0.9.The lowest rated label was "battery life" for the
HUAWEI P40, which indicated that consumers have
higher expectations for the battery life of this phone.
3.2 Consumers are More Diversified in
Terms of what they Care about
HUAWEI Phones
As of July 1, 2021, Mac Form collected 492 reviews
of the HUAWEI P40 model phone.
With multiple choices available to respondents,
the relevant high-frequency words are shown in
Table 1.
As shown in the table, the most frequent words
are "appearance" and " running speed", indicating
that consumers were more concerned about the
appearance of the phone on the outside and the
speed of the phone on the inside. From the
comparison of the results of sentiment analysis,
consumers were more optimistic about these two
features.
Secondly, the emergence of the terms "Cost
performance" and "Hongmeng system" is also
noteworthy. Consumers who buy HUAWEI phones
were also more concerned about the
price/performance ratio of domestic phones and
whether to use the domestic operating system
Hongmeng, with a certain subjective atiitude and
personal preference.
4 CONCLUSION
This paper used natural language processing
technology to carry out machine learning through
CNN model, in order to evaluate the products on
e-commerce platform, which taked HUAWEI P40
model mobile phone in HUAWEI Mall as an
example. The relative reviews were classified and
scored by tags, finally review sentiment analysis is
conducted. The sentiment analysis initially reveal
consumers' sentiment attitude towards the phone.
There are two conclusions and recommendations.
(1) Consumers focus on 6 aspects of
HUAWEI phones, such as "appearance", "running
speed" and "photography", and the proportion of
these three items is greater than 50%, indicating that
consumer preferences are more convergent. Mobile
phone manufacturers can improve and stabilize the
performance of these three aspects to achieve a
better user experience.
(2) Consumers' preferences for HUAWEI P40
mobile phone's 6 performance items
all showed a positive trend, indicating that users'
trust and love of this phone were high. This
phenomenn indicates that users had a high degree of
trust and affection for this phone, and the user
stickiness was high at this time. Producers can
accelerate the development of new byproducts,
which is to further expand the market and enhance
consumers' purchasing power.
The limitation of this paper is mainly the
limitation of the analysis sample.
The majority of reviews were filled in before
consumers had fully experienced the product
features, and it is possible that with the passage of
time, the consumer attitudes might still fluctuate
over time. Secondly, the survey sample was limited
by the number of HUAWEI P40 users, which made
it difficult to accurately collect questions, the
questionnaire respondents were HUAWEI mobile
phone users. Although it can largely represent the
attitude of HUAWEI product consumers towards
HUAWEI mobile phones, the classification of this
label was still not precise enough and may not
reflect 100% of consumers' concerns about the
Sentiment Analysis of Product Reviews on E-Commerce Platforms Based on NLP: Take HUAWEI P40 as an Example
805
HUAWEI P40. With the further promotion of
HUAWEI P40 mobile phone in the market, we can
use more diversified e-commerce platforms to
collect different samples for analysis in a stratified
manner.
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