Sentimental analysis of customer product Reviews to understand customer needs using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| বিন্যাস: | গবেষণাপত্র |
| ভাষা: | English |
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Brac University
2023
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| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/21980 |
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10361-21980 |
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10361-219802023-12-14T21:02:37Z Sentimental analysis of customer product Reviews to understand customer needs using machine learning Symum, Md Abdullah Al Sheemu, Subarna Yeasmin Asif, Abu Saleh Md. Islam, Konika Shakil, Arif Department of Computer Science and Engineering, Brac University Natural language processing Customer feedback Textual data analysis Sentimental analysis Transformer Sentiment lexicons Word embeddings Machine learning Natural language processing (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-44). People are influencing aspects of the digital world through machines. As a result, it is crucial to upgrade and use this aspect to do so. In the past, people used written letters to provide feedback. However, people are now posting these reviews to the seller’s page directly on the internet.In the digital age, user feedback, and reviews have a significant impact on shaping businesses. However, it is challenging to ana- lyze and understand the sentiments conveyed owing to the large volume of data and the presence of spam.If we can develop automated systems that can interpret senti- ments of people and emotions from user reviews, which would help to leave a great impact on improving their marketing strategies and can understand the require- ments of customer. However, machines are constrained by binary language, and, thus faces difficulties in comprehending human emotions and thoughts.By leverag- ing machine learning algorithms for sentiment analysis, we aim to evaluate sentiment in a vast collection of customer reviews. Sentiment analysis is an essential domain in machine learning and natural language processing, which focuses on identifying and classifying sentiments, opinions, and emotions expressed in textual data. This paper presents a comprehensive overview of sentiment analysis within the frame- work of machine learning approaches. For sentiment analysis, a wide variety of machine learning techniques and methods have been studied, including more estab- lished methods like deep learning models such as Convolutional Neural Networks (CNNs) and Transformers like BERT as well as traditional approaches like Naive Bayes and linear Support Vector Machines (SVM), KNN, and logistic regression. . The paper also addresses the challenges associated with sentimental analysis, such as data preprocessing, extracting features, and selection of models. Furthermore, it emphasizes the significance of labeled data and underscores the role of sentiment lexicons and word embeddings in improving sentiment analysis performance. The paper concludes by discussing the prospects of sentiment analysis in machine learn- ing, highlighting its significance in social media analysis, customer feedback analysis, and market research. Therefore, the research outcomes of our paper provide valuable insights for companies that would enable them to enhance their marketing strategies and improve their products to meet customer requirements more effectively based on the evaluation of customer reviews and feedback. Md Abdullah Al Symum Subarna Yeasmin Sheemu Abu Saleh Md. Asif Konika Islam B.Sc. in Computer Science and Engineering 2023-12-14T05:47:47Z 2023-12-14T05:47:47Z 2023 2023-05 Thesis ID 18201007 ID 19101297 ID 19301125 ID 17201007 http://hdl.handle.net/10361/21980 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 44 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Natural language processing Customer feedback Textual data analysis Sentimental analysis Transformer Sentiment lexicons Word embeddings Machine learning Natural language processing (Computer science) |
| spellingShingle |
Natural language processing Customer feedback Textual data analysis Sentimental analysis Transformer Sentiment lexicons Word embeddings Machine learning Natural language processing (Computer science) Symum, Md Abdullah Al Sheemu, Subarna Yeasmin Asif, Abu Saleh Md. Islam, Konika Sentimental analysis of customer product Reviews to understand customer needs using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Shakil, Arif |
| author_facet |
Shakil, Arif Symum, Md Abdullah Al Sheemu, Subarna Yeasmin Asif, Abu Saleh Md. Islam, Konika |
| format |
Thesis |
| author |
Symum, Md Abdullah Al Sheemu, Subarna Yeasmin Asif, Abu Saleh Md. Islam, Konika |
| author_sort |
Symum, Md Abdullah Al |
| title |
Sentimental analysis of customer product Reviews to understand customer needs using machine learning |
| title_short |
Sentimental analysis of customer product Reviews to understand customer needs using machine learning |
| title_full |
Sentimental analysis of customer product Reviews to understand customer needs using machine learning |
| title_fullStr |
Sentimental analysis of customer product Reviews to understand customer needs using machine learning |
| title_full_unstemmed |
Sentimental analysis of customer product Reviews to understand customer needs using machine learning |
| title_sort |
sentimental analysis of customer product reviews to understand customer needs using machine learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21980 |
| work_keys_str_mv |
AT symummdabdullahal sentimentalanalysisofcustomerproductreviewstounderstandcustomerneedsusingmachinelearning AT sheemusubarnayeasmin sentimentalanalysisofcustomerproductreviewstounderstandcustomerneedsusingmachinelearning AT asifabusalehmd sentimentalanalysisofcustomerproductreviewstounderstandcustomerneedsusingmachinelearning AT islamkonika sentimentalanalysisofcustomerproductreviewstounderstandcustomerneedsusingmachinelearning |
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