Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user's review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user's. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%

An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique

Sentiment Classification is a key area of natural language processing research that is frequently utilized in several industries. The goal of sentiment analysis is to figure out if a product or service received a negative or positive response. Sentiment analysis is widely utilized in several commercial fields to enhance the quality of services (QoS) for goods or services by gaining a better knowledge of consumer feedback. Deep learning provides cutting-edge achievements in a variety of complex fields. The goal of the study is to propose an improved approach for evaluating and categorising sentiments into different groups. This study proposes a novel hybridised model that combines the benefits of deep learning technologies Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with the word embedding technique. The performance of three distinct word embedding approaches is compared in order to choose the optimal embedding for the proposed model's implementation. In addition, attention-based BiLSTM is used in a multi-convolutional approach. Standard measures were used to verify the validity of the suggested model's performance. The results show that the proposed model has a significantly enhanced accuracy of 96.56%, which is significantly better than existing models

A Comparative Detailed Study of Data Mining Methods and Tools in Data Warehouse

Now-a-days we have a tendency to be in modern era. There are immense quantity of knowledge and knowledge, that to be collected from completely different sources and analyzed to urge the information. Once grouping the info from numerous sources, it's keep in huge repositories, that is thought as knowledge warehouse. There is variety of techniques wont to extract the knowledge from data warehouse and analyze to urge the important information that is thought as data processing. For this purpose, we have a tendency to use completely different data processing tools like wood hen, KEEL, R, KNIME, ORANGE etc. During this paper we are going to compare completely different data processing technique and tools for maintenance in knowledge warehouse