VIET NAM NATIONAL UNIVERSITY HO CHI MINH CITY UNIVERSITY OF INFORMATION TECHNOLOGY FACULTY OF INFORMATION SYSTEMS NGUYEN THI KHANH HA - 18520692 VU THI QUY- 18521317 GRADUATION THESIS SENTIMENT ANALYSIS OF CUSTOMER REVIEWS OF FOOD DELIVERY SERVICES INFORMATION SYSTEMS ENGINEERING INSTRUCTOR PhD DO TRONG HOP MSc NGUYEN THU THUY HO CHI MINH CITY, 2023 ACKNOWLEDGMENT In order to complete this graduation thesis, in addition to our personal efforts and unwavering commitment, it is imperative to acknowledge the indispensable support and assistance rendered by the faculty members at the University of Information Technology, VNU-HCMC. We would like to express our profound and sincere appreciation to PhD. Do Trong Hop, our esteemed supervisor, who wholeheartedly aided us from the inception of our Deep Learning studies. Hop's unwavering trust and encouragement during challenging times throughout the thesis composition have been invaluable.
Furthermore, we are immensely grateful for his insightful contributions from the early stages of topic selection. His astute guidance has accompanied us throughout the entire research and writing process of this thesis. We could not have envisioned an exemplary advisor and mentor for our academic pursuit. Although we have diligently endeavored to acquire knowledge, conduct research, experiment, and achieve initial promising outcomes, the inherent limitations in our expertise and experience necessitate the anticipation of constructive feedback to refine and enhance the thesis.
Advisor Nguyễn Thi Khánh Hà Vũ Thị Quý Contents Chapter 1 INTRODUCTION. Goal and Study SCOpe.-c-cc St the 15 Chapter 2 THEORETICAL FOUNDATIONS. Overview of the Analysis Problem.------c+ce-xeeeeeeesrseeeexee LT 2.ceceeececec ees eeee sees neeeeeereeeeeeasseessasseeseseseaeseeneseseee 22 2. Current Approaches in Sentiment Analysis 0.
Topic Modeling Approaches. Algorithm and Fundamental Concept. RNN, LSTM and Transformer Arehitecture. Pretrained BERT Model Theory .:----+:-+cc++c+cccx+se+ 47 2.
The theory of PhoBERT pretraining model. The theory of LDA model.ceeceesccesesseseeseseesesesesseseseeaesesesesesessesesaeseeneaes 55 Chapter 3 THEORETICAL DATASET AND SOLUTION APPROACH. cà HH Hi 8 3. Introduction to the Training Dataset.
Process and Algorithms used in Sentiment AnalySis. Tokenization and Encoding. Sentiment Analysis Model building. --- - c5++cscecxsxererxey 75 Chapter 4 SYSTEM DESIGN & IMPLEMENTATION.
Overview of System Design. Deployment of System. Systems to be Deployed and Technologies Ủsed.3 Visualization of Analytical Results.-----¿-¿55+5x+sxccxscxccxe 107 Chapter 5 RESULTS AND CONCLUSIONS. LiL ati Tan SR AF osssecsvensensensceseasenssveasessesvesvensensensenseas 113 LIST OF FIGURES Figure 1-1 Statista.
Online food delivery in Vietnam Report [1]. 11 Figure 1-2 General Online Review Statistics [2]. eccessssessesesesseseceeeseeeeseseeeeneceseaeeeeeeeeeeees 12 Figure 2-1 Uncover emotion: Social media sentiment analysis [3].----------‹-- 18 Figure 2-2 Word example 011712777. 20 Figure 2-3 Visual illustration of undeleted word cloud Stop word in English.
21 Figure 2-4 Sentiment analysis levels. cseessessesesseeseesesseeseeseesnsceeesceeceaesaeeaeeasenscceceueeeeenteneenss 24 Figure 2-5 Overview of the sentiment analysis approaches.---- - - s+c+csxc+xsxerxex 27 Figure 2-6 The chronological release of these proposed models over the years. 29 Figure 2-7 Structure of words in Vietnamese. cecssessssssessseseeseeseessesseasessesecseesesneeaseaeenee 31 Figure 2-8 Tokemization.ceccecccccccseesessessesesseeseceseesecsesessecsesssessecussessecseseesesseeeseesesnsseeneeeseeneeneess 34 Figure 2-9 Compare the difference of word separation at different levels.- 35 Figure 2-10 A simple RÌNN.-- cà HH HH gàng nghe 39 Figure 2-11 Visualization of the repeating component of an LSTM Network.
42 Figure 2-12 RNN, LSTM and Transformer 1lÏustrafiOn. --5- +55 5++cs>xszsrezxsrxersree 43 Figure 2-13 Architecture of Transformers Neural NetWOrK.-- «5c cs+csrererkerererree 45 Figure 2-14 BERT input representation. cceecessessesesseesesseseesececsessececsecseeseceeseenecucseeeesecaeeneeesens 48 Figure 2-15 BERT pre-training and fine-funing. 53 Figure 3-1 Overview of the data processing pipeline in the system to address the three tasks of Word Cloud, Topic Modeling, and Sentiment Analys1.-----++c+ccszssce2 57 Figure 3-2 Sentiment Analysis Processing.
5-52-1111 kgrkrree 61 Figure 3-3 Example of lowercase converSion 1n DF€DTOC€SSITE. ---5- 5+ 5++c+s>c+x 65 Figure 3-4 Emojis on Facebook .------:--++-++++++k+rxt+kt+ktrkttrkrkktrktrkkrtrrkrrrrerrrkrrrerke 66 Figure 3-5 Example of tOKefniZafIOIA. 72 Figure 3-6 Compare result between different fOOÌS. -- ¿5+ 5++2+2z+vczvrtrterxerrrertersrree 73 Figure 3-7 Number of tokens per COMMENC.sessesessecseseessesecsesesseescseeaeenececaecnecuceeenecueaeeneeneess 74 Figure 3-8 WordCloud Processing .---¿- 2 <5 5< kề SE E11 1111111111111 ree 93 Figure 3-9 Word cloud image r€SuÏÍ.
- --¿- +5 5++5++++EE2EEYSESEEEEeEkerrktrkerrrrrkrrkrrrrkrrkrrrree 96 Figure 3-10 Topic modeling Sf€p. rkee 97 Figure 3-12 Results of the model €X€CUtIOI.- -- 2-52 25+‡E‡ESEeEkerkerkerkrrkererkerkrrrrrk 100 Figure 4-1 illustrates the batch processing WOrkfÏOW.-------ccccxcreerrsrrrrrererrrerrek 103 Figure 4-2 Overview system architecture dia8TaIm.-- se +xsx++xsxeexererxerkerkrrrrk 104 Figure 4-3 Dashboatd .cceccccessssesssssssssseesessssessessesscsessecuescsesuessesecssansacsesucsessecuesuesecusseeseeneaeeneeneenes 109 Figure 4-4 Dashboard of result. ceccececsessecsessessessnessessessecsessssueenesneeseeneeaeeaeeseeassesseeeeeeeeeneeaeenees 111 LIST OF TABLES Table 1: Comparison of Transformer IIO(@ÏS. SG St 50 Table 2: LDA Result f(LÏ€.
So 1 vn TH ng TH nh ghHhg 101 Table 3: PostgreSQL database. SG 33kg ket 105 Table 4: Tables of the database Structure. S531 SEEEESkeeeerereeereese 107 LIST OF ACRONYMS AI: Artificial Intelligence API: Application Programming Interface ANN: Artificial Neural Network CNN: Convolutional Neural Network DBMS: Database Management System MLP: Multilayer Perceptrons NN: Neural Network FC Layer: Full-connected Layer RNN: Recurrent Neural Network ReLU: Rectified Linear Unit ResNet: Residual Network ABTRACT Sentiment analysis plays a crucial role in understanding and evaluating customer opinions and feedback. In the context of food delivery services, analyzing customer reviews can provide valuable insights into the overall satisfaction and sentiment of customers towards the services provided.
This study focuses on sentiment analysis of customer reviews specifically related to food delivery services. The aim is to analyze and classify customer sentiments as positive, neutral, or negative based on their reviews. By applying natural language processing techniques and machine learning algorithms, the study aims to extract meaningful information from textual data and identify the sentiment expressed by customers. The analysis of customer reviews can help service providers gain a deeper understanding of customer preferences, identify areas of improvement, and make informed decisions to enhance the quality of their services.
It also enables them to address customer concerns and complaints promptly, thereby improving customer satisfaction and loyalty. The findings of this sentiment analysis can be used by food delivery service providers to monitor customer sentiments over time, track the effectiveness of service improvements, and make data-driven decisions to enhance their overall customer experience. In conclusion, sentiment analysis of customer reviews of food delivery services provides valuable insights into customer sentiment, allowing service providers to make informed decisions and improvements to enhance customer satisfaction and loyalty.1 Problem statement Vietnam's culinary landscape is widely acknowledged for its diverse offerings and embodies a dynamic food culture. Food holds deep significance within Vietnamese society and has undergone transformative changes over time.
Examining the market trends, the online food delivery industry in Vietnam experienced remarkable growth from 2016 to 2020, with an annual growth rate of 96. However, from 2021 to 2025, the growth rate gradually decreased to 35.8 percent per year. Despite this decline, it is projected that by 2025, the Vietnamese food delivery market will attain a substantial value of 2,709.7 million USD, reflecting the sustained demand and potential in the future of the industry. Online food delivery in Vietnam Report [1].
In recent years, a notable shift in consumer preferences and behaviors can be observed, as contemporary Vietnamese consumers increasingly gravitate towards digital modes of consumption, driven by their fast-paced lifestyles that prioritize convenience. This shift has resulted in a significant surge in the demand for online food delivery services in Vietnam. 11 The emergence of online platforms has bestowed customers with the power to voice their opinions, both positive and negative, at any hour of the day. According to a recent survey conducted by Podium, nearly everyone (93%) say that an online review has impacted their purchase habits.
More details, consumers expect high standards from the brands they do business, with most saying they will not engage with a business or product that has less than a 3. In order to uphold their reputation, enhance the guest experience, and avert negative trends from affecting sales, food brands are increasingly resorting to sentiment analysis as a valuable tool. of consumers say online reviews impact purchase decisions of consumers say content ofa review 82% has convinced them to make a purchase of consumers say online reviews for local businesses are 80% as helpful as product reviews on sites like Amazon.com on average, is the minimum star rating of a business 3 consumers would consider engaginng with Figure 1-2 General Online Review Statistics [2] Nevertheless, manually scrutinizing and interpreting customer reviews of online food delivery services is an onerous task for businesses due to various factors. Firstly, there is a voluminous amount of textual data to process and interpret, which can be time- consuming and laborious for businesses.
Additionally, customer reviews are often informal and lack a structured format, making it arduous to extract meaningful insights from the data. Secondly, different customers may express their opinions and sentiments in various ways, including sarcasm, irony, and humor, which may necessitate additional effort to comprehend accurately. Thirdly, businesses may encounter difficulties in detecting and categorizing different aspects of customer satisfaction, such as food quality, delivery speed, and customer service. Lastly, businesses must stay abreast of the evolving trends in customer preferences and needs, which exacerbates the complexity of manual analysis.
Problem Solution In this era where the confidence index is paramount, businesses require effective tools and techniques to analyze and comprehend customer reviews of online food delivery services in Vietnam. Sentiment analysis, a Natural Language Processing technique, can automatically classify the polarity of textual data, providing insights into the emotions and opinions expressed in customer reviews. By leveraging sentiment analysis, businesses can promptly and proficiently process extensive volumes of customer reviews, discern patterns and trends in customer feedback, extract meaningful insights, and enhance their business strategies accordingly. The problem of sentiment analysis can be regarded as a subtask within the field of Natural Language Processing (NLP), aiming to extract the affective states and subjective opinions expressed by individuals through textual means.
Numerous research endeavors have been dedicated to developing applications based on sentiment analysis. However, the task of comprehending human sentiment is inherently intricate. In practice, it necessitates discerning the underlying intentions of commenters, such as identifying instances of irony, sarcasm, or subjectivity embedded within the text. Moreover, user-generated review often diverges from the formal linguistic conventions found in traditional literature or journalism, exhibiting a range of linguistic imperfections such as orthographic errors, informal vernacular, slang expressions, or abbreviations.
Furthermore, due to the target domain of natural language data processing and analysis, each language exhibits distinct characteristics that warrant diverse technological approaches and preprocessing procedures. Consequently, despite the abundance of solutions proposed for sentiment analysis, predominantly tailored for the English language, their direct applicability to sentiment analysis tasks involving Vietnamese comment data remains limited. Therefore, the application of sentiment 13 analysis models demands adaptability and customization to address the intricacies of Vietnamese language data effectively. Goal and Study Scope The first important objective of this study is to analyze thoroughly the Vietnamese people's comments on the Food Finding and Reviewing website.
Comments are a valuable source of opinions and feedback that users provide after using a product, service, or visiting a place, and they can be positive, negative, or neutral. In this thesis, we will craw food reviews from the Foody website in all of the cities and provinces in Vietnam, which is a prominent platform for reviewing and searching for food locations across most provinces and cities in Vietnam. Furthermore, a crucial aim of this research endeavor is to employ a diverse array of analytical techniques, including Word Cloud, Latent Dirichlet Allocation, as well as LSTM and PhoBERT sentiment analysis models, to visualize the outcomes comprehensively. This multifaceted visualization approach facilitates an in-depth examination of the sentiment distribution inherent in the comments, enabling the identification of pivotal patterns and trends that hold relevance for informed business decision-making.
The integration of these advanced analytical methods serves to elucidate the sentiment landscape from a macroscopic overview to intricate details, thereby fostering a holistic understanding of the data. Finally, we will build a system that can automate the entire process of data processing, analysis, and displaying the results on a web platform. This system will help us to efficiently analyze a large volume of comments and present the results in a user- friendly and interactive way, allowing users to interact with the data and gain deeper insights.