A hybrid convolutional long short-term memory (CNN-LSTM) based natural language processing (NLP) model for sentiment analysis of customer product reviews in Bangla

Abstract

Sentiment Analysis (SA) examines how people feel about products, services, people, organizations, events etc. Most of the Natural language processing on SA research has focused on English. In the case of Bangla, it lacks sufficient study as well as a proper dataset. All previous analyses employed KNN, NB, and other methods. We develop a natural language processing (NLP) model for separating opinion and sentiment from Bangla customer surveys. This technology isolates extreme client opinions to help with business and marketing decisions. Bangladesh is embracing e-commerce and f-commerce. Client comments and evaluations are becoming more significant in judging product or service quality, and this industry is evolving toward internet distribution. Organizations utilize client audits to check product quality. Our objective is to systematically collect client feedback and understand their product reaction. We used a hybrid CNN-LSTM based NLP model to classify Bangla texts in 3 viewpoint categories (positive, negative and neutral). We tested our model using a Bangla dataset that we generated. For our dataset, we collected polls and comments from websites and social media. Finally, among the three evaluation matrices, the f-1 score is providing the highest average, and the three-opinion technique is 87.22 percent accurate in determining the performance of our task.

Subject Classification:

• Primary 65C99
• Secondary 62K99

Keywords:

• Customer review analysis
• Opinion mining
• Hybrid CNN-LSTM architecture
• Bangla sentiment analysis
• NLP