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

Hossain, Md. Sabbir; Nayla, Nishat; Rassel, Annajiat Alim

doi:10.1109/ciss53076.2022.9751188

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…The analysis of Banglish text on social media in Bangladesh employed NLP techniques and machine learning models for product market demand assessment. Results indicated high accuracy in demand analysis, providing valuable insights into popular smartphone choices by gender in the Bangladeshi market [152]. The study on managerial responses to online customer complaints and negative reviews integrated justice theory and service recovery literature.…”

Section: Marketing and Brand Managementmentioning

confidence: 90%

“…The creation of an Amazon customer reputation system [138] and the study of a comprehensive approach to reputation generation from customer reviews [139] emphasize the importance of sentiment analysis in brand image management. NLP is used to analyze smartphone demand using social media data [152], demonstrating the importance of sentiment analysis in marketing and customer preferences. Online customer review NLP applications demonstrate language processing technologyʹ adaptability and influence.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2023

Preprint

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In recent years, e-commerce platforms have replaced conventional marketplaces. People are fast adopting online shopping since they can purchase from home. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer evaluations, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges. This study covers 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the bulk of existing research relies on Amazon user reviews. Additionally, recent research has stressed the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates researchers' expanding interest and the field's continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

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Section: Marketing and Brand Managementmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

View full text Add to dashboard Cite

In recent years, e-commerce platforms have replaced conventional marketplaces. People are rapidly adopting internet shopping due to the convenience of making purchases from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

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“…The system flow can be seen briefly in Figure 1. This research begins with collecting data in the form of tweets mentioned from the Altmetric website using Instant Data Scraper, an extension application from Google Chrome which is then stored in CSV configuration [11]. Tweet data is in English, with 10,000 tweets discussing the paper The Proximal Origin of SARS-CoV-2, which has the highest score based on Altmetric website calculations [12].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article

Fakhrezi

Rochim

Nugraheni

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The assessment of academic articles is based on the number of citations, but the number only is not enough. So now there is Altmetric which can measure the impact of academic articles from the number of citations and using social media, usually Twitter. Still, the number of mentions on Twitter is not enough because the expressions of the sentences vary. Mentions must be classified according to neutral, positive, and negative criteria. Sentiment analysis is performed on tweets to measure social media volume and attention related to research findings from academic articles. There are many sentiment analysis methods, so this study aims to compare sentiment analysis methods using Decision Tree, K-NN, Naïve Bayes, and Random Forest to get the most suitable methods. The evaluation method in this study uses the Confusion Matrix by searching for Accuracy, Precision, and Recall values. The results show that the most suitable sentiment analysis method is Naïve Bayes by obtaining the highest classification suitability value of the other methods, which has an actual positive sentiment value of neutral 2056, positive 1200, and negative 1292. In addition, Naïve Bayes gets the highest accuracy score of 95, 45%.

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

Cited by 11 publications

References 12 publications

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article

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