Product Review Sentiment Analysis by Using NLP and Machine Learning in Bangla Language

Shafin, Minhajul Abedin; Hasan, Md. Mehedi; Alam, Md. Rejaul; Mithu, Mosaddek Ali; Nur, Arafat Ulllah; Faruk, Md. Omar

doi:10.1109/iccit51783.2020.9392733

Cited by 28 publications

(7 citation statements)

References 8 publications

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“…In a prior study KNN, Decision Tree, Random Forest performed well with similar accuracy but SVM and Logistic Regression gives higher accuracy for sentiment analysis in Bangla language [10]. Online engagement of Bangla language in the business sector is increasing day by day and it is very rational to use sentiment analysis of positive and negative feedback written in Bangla language performing natural language processing and five traditional machine learning algorithms to perform higher precisions [11]. Nowadays the usage of acronyms and abbreviations on social media is increasing and this is broadly used by teenagers and young adults.…”

Section: Related Workmentioning

confidence: 92%

“…Online opinions are changing the way businesses are conducted, and a large amount of data is generated each year that is underutilized [13]. A machine learning based system can help customers have a better online shopping experience by allowing them to go through the system for product reviews based on the ratio of positive and negative feedback from previous customers [14]. From the above discussion, it is clearly observed that most of the research in this field designed to predict stock price prediction and most of the Bangla language works are based on sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

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Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition

Hossain¹,

Nayla²,

Rasel³

2022

Preprint

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Product market demand analysis plays a significant role for originating business strategies due to its noticeable impact on the competitive business field. Furthermore, there are roughly 228 million native Bengali speakers, the majority of whom use Banglish text to interact with one another on social media. Consumers are buying and evaluating items on social media with Banglish text as social media emerges as an online marketplace for entrepreneurs. People use social media to find preferred smartphone brands and models by sharing their positive and bad experiences with them. For this reason, our goal is to gather Banglish text data and use sentiment analysis and named entity identification to assess Bangladeshi market demand for smartphones in order to determine the most popular smartphones by gender. We scraped product related data from social media with instant data scrapers and crawled data from Wikipedia and other sites for product information with python web scrapers. Using Python's Pandas and Seaborn libraries, the raw data is filtered using NLP methods. To train our datasets for named entity recognition, we utilized Spacey's custom NER model, Amazon Comprehend Custom NER. A tensorflow sequential model was deployed with parameter tweaking for sentiment analysis. Meanwhile, we used the Google Cloud Translation API to estimate the gender of the reviewers using the BanglaLinga library. In this article, we use natural language processing (NLP) approaches and several machine learning models to identify the most in-demand items and services in the Bangladeshi market. Our model has an accuracy of 87.99% in Spacy Custom Named Entity recognition, 95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for demand analysis. After Spacy's study, we were able to manage 80% of mistakes related to misspelled words using a mix of Levenshtein distance and ratio algorithms.

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Section: Related Workmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition

Hossain¹,

Nayla²,

Rasel³

2022

Preprint

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“…The study showed significant achievements by employing a K-Nearest Neighbours (KNN) model, which demonstrated an impressive accuracy rate of 96%. In the context of binary classification, the authors [17] used Support Vector Machines (SVM) as their chosen algorithm. They reported an accuracy rate of 88.81% using a dataset consisting of 1020 instances.…”

Section: Literature Reviewmentioning

confidence: 99%

Mining User Opinions: A Balanced Bangla Sentiment Analysis Dataset for E-Commerce

Shanto,

Ahmed,

Jony

2023

Malaysian J. Sci. Adv. Tech.

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The utilization of sentiment analysis has gained significant importance as a valuable method for obtaining meaningful insights from textual data. The research progress in languages such as English and Chinese has been notable. However, there is a noticeable dearth of attention towards creating tools for sentiment analysis in the Bangla language. Currently, datasets are limited for Bangla sentiment analysis, especially balanced datasets capturing both binary and multiclass sentiment for e-commerce applications. This paper introduces a new sentiment analysis dataset from the popular Bangladeshi e-commerce site “Daraz”. The dataset contains 1000 reviews across 5 product categories, with both binary (positive/negative) and multiclass (very positive, positive, negative, very negative) sentiment labels manually annotated by native Bangla speakers. Reviews were collected using an organized process, and labels were assigned based on standardized criteria to ensure accuracy. In addition, a benchmark evaluation of the performance achieved by Machine Learning and Deep Learning algorithms on this dataset is also provided. The new dataset can aid research on multiclass and binary Bangla sentiment analysis utilizing both machine learning, deep learning, and Large Language Models. It can aid e-commerce platforms in analysing nuanced user opinions and emotions from online reviews. The utilization of categorized product reviews also facilitates research in the field of text categorization.

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“…Where, P(A|B) = probability of A on observed event B P(B|A) = probability of B given A is true P(A) = probability of A P(B) = probability of 2) Logistic Regression logistic regression is used when the dependent variable or target is categorical, that is, either true or false [32]. Some examples where logistic regression can be used is when we need to classify if a piece of information is true or false, or if the sentiment of a text is sad or happy.…”

Section: ) Naïve Bayesmentioning

confidence: 99%

Chrome Extension for Misinformation Detection

Borges¹

2022

AJCT

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Misinformation can be stories, hoaxes, or news deliberately created to spread false news and deceive readers. Fake news has always been a part of our lives. However, it has become a topic of interest only recently. Majorly due to the rise of SOCIAL MEDIA. As stated in news articles the Supreme Court condemns these actions and advice a regulatory mechanism. As the use of social media has increased so, has the number of unreliable sources. During covid, there was a variety of misinformation floating around like, applying cow dung can cure covid. In reality, cow dung can't cure covid but can cause black fungal infection. Some other examples include inciting religious sentiments, causing chaos, and harming someone's reputation. Therefore, detecting fake news and stopping it from spreading is necessary. In this model, I have applied TF-IDF vectorizer and Passive Aggressive Classifiers to train my model. After the training and testing have been completed, a local server using Flask has been set up to assist in the development of the second phase of the project, which is Chrome Extension. The Chrome Extension is called Gossip Checker and sends selected data to the model and returns a predicted score of 0 and 1, where 0 means the data is reliable and 1 is not.

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Product Review Sentiment Analysis by Using NLP and Machine Learning in Bangla Language

Cited by 28 publications

References 8 publications

Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition

Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition

Mining User Opinions: A Balanced Bangla Sentiment Analysis Dataset for E-Commerce

Chrome Extension for Misinformation Detection

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