Online shopping is becoming one of the most de-manding everyday needs, nowadays. These days people are feeling comfortable shopping online. The number of its customers is increasing day by day as well as raising some problems. The major problem is that the customers can not choose the quality-full product by reading every review of an online product. Besides, the product reviews are helpful to improve the services of an e-commerce site but required huge manpower and time. We have focused on Bangla text and aimed to solve these problems by the application of Deep Neural Network (DNN) and Natural Language Processing (NLP). In this study, we have proposed two deep learning NLP models: one is for sentiment analysis and the other one is for Product Review Classification intended to improve both the quality and services. Significantly, our proposed models result in high accuracy: 0.84 and 0.69 for both Sentiment Analysis and Product Review Classification, respectively. Undoubtedly, these models can help the customers to choose the right product and the service provider to improve their services.

Sentiment Analysis and Product Review Classification in E-commerce Platform

Last few decades, the availability and accessibility of the Bangla document and its content have rapidly increased due to the rapid technological advancement. Intense research needs to be performed on various Bangla documents due to the diversity of the language and associated sentiment. Document classification is one of the fundamental problems of Natural Language Processing. To handle miss-classification and convenient indexing and searching of Bangla documents on the web, researchers nowadays exploring different fields of computer science to classify Bangla documents. In this paper, Deep Learning based approaches are implemented to classify Bangla text documents. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is used here for the classification task. Here we have implemented an advanced technique that encoded the documents at their character level. Documents from three different data sources are used to validate and test of the working models. The highest classification accuracy is 95.42% that is achieved on the Prothom Alo data set using LSTM. Furthermore, we presented a comparison between two models and explained how well the classification task can be carried out using our character level approach with higher accuracy.

Bangla Document Classification using Character Level Deep Learning

Emotion is the most important gear for human textual communication with each other via social media. Nowadays, people use text for reviewing or recommending things, sharing opinions, rating their choices or unlikeness, providing feedback for different services, and so on. Bangladeshi people use Bangla to express their emotions. Current research based on sentiment analysis has got low-performance output by using several approaches on detecting sentiment polarity and emotion from Bangla texts. In this study, we have developed four models with the hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) with various Word Embeddings including Embedding Layer, Word2Vec, Global Vectors (Glove), and Continuous Bag of Words (CBOW) to detect emotion from Bangla texts (words, sentences). Our models can define the basic three emotions; happiness, anger, and sadness. It will make interaction lively and interesting. Our comparisons are bestowed against CNN, LSTM with different Word Embeddings, and also against some previous researches with the same dataset based on classical Machine Learning techniques such as Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (K-NN). In our proposed study, we have used Facebook Bangla comments for a suitable dataset. In our study, we have tried to detect the exact emotion from the text. And in result, the best model integrating Word2Vec embedding layer with a hybrid of CNN-LSTM detected emotions from raw textual data with an accuracy of 90.49% and F1 score of 92.83%.

Sentiment Analysis of Bangla Language Using Deep Learning Approaches

Text classification has a growing interest among NLP researchers due to its tremendous availability on online platforms and emergence on various Web 2.0 applications. Recently, text classification in resource-constrained languages has been bringing much attention due to the sharp increase of digital resources. This paper presents a CNN based text classification model for one of the low resource languages like Bengali. The goal of the Bengali text classification is to assign a particular category to a text into one of the pre-defined categories based on its semantic and syntactic meaning. The proposed system comprises of four key modules: embedding model generation, Text to feature representation, training, and testing. The classification system trained and validated with 39, 079 and 6, 000 text datasets. Experimental evaluation with 9, 779 test datasets shows the accuracy of \(96.85\%\), which indicates the superior performance compared to the existing techniques.

Text Classification Using Convolution Neural Networks with FastText Embedding

In modern times, ensuring social security has become the prime concern for security administrators. The widespread and recurrent use of social media sites is creating a huge risk for the lives of the general people, as these sites are frequently becoming potential sources of the organization of various types of immoral events. For protecting society from these dangers, a prior detection system which can effectively detect events by analyzing these social media data is essential. However, automating the process of event detection has been difficult, as existing processes must account for diverse writing styles, languages, dialects, post lengths, and et cetera. To overcome these difficulties, we developed an effective model for detecting events, which, for our purposes, were classified as either protesting, celebrating, religious, or neutral, using Bengali and Banglish Facebook posts. At first, the collected posts’ text were processed for language detection, and then, detected posts were pre-processed using stopwords removal and tokenization. Features were then extracted from these pre-processed texts using three sub-processes: filtering, phrase matching of specific events, and sentiment analysis. The collected features were ultimately used to train our Bernoulli Naive Bayes classification model, which was capable of detecting events with 90.41% accuracy (for Bengali-language posts) and 70% (for the Banglish-form posts). For evaluating the effectiveness of our proposed model more precisely, we compared it with two other classifiers: Support Vector Machine and Decision Tree.

Using Machine Learning to Detect Events on the Basis of Bengali and Banglish Facebook Posts

Identifying and categorizing opinions in a sentence is the most prominent branch of natural language processing. It deals with the text classification to determine the intention of the author of the text. The intention can be for the presentation of happiness, sadness, patriotism, disgust, advice, etc. Most of the research work on opinion or sentiment analysis is in the English language. Bengali corpus is increasing day by day. A large number of online News portals publish their articles in Bengali language and a few News portals have the comment section that allows expressing the opinion of people. Here a research work has been done on Bengali Sports news comments published in different newspapers to train a deep learning model that will be able to categorize a comment according to its sentiment. Comments are collected and separated based on immanent sentiment. The deep learning algorithms that have been used are Convolutional Neural Network (CNN), Multilayer Perceptron, Long Short-Term Memory (LSTM). General Terms Sentiment Analysis, Deep Learning, Emotion Classification

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Zillur Rahman Saurav

Papers

Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique