The rapid growth of social media and specialized websites that provide critical product reviews has resulted in a massive collection of information for customers worldwide. These data could contain a wealth of information, such as product reviews, market forecasting, and the polarity of sentiments. In these challenges, machine learning and deep learning algorithms give the necessary capabilities for sentiment analysis. In today’s competitive markets, it’s critical to grasp reviewer opinions and sentiments by extracting and analyzing their characteristics. The research aims to develop an optimised model for evaluating sentiments and categorising them into proper categories. This research proposes a unique, novel hybridised model that integrates the advantages of deep learning methods Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with word embedding technique. The performance of different word embedding techniques is compared to select the best embedding for the implementation in the proposed model. Furthermore, a multi-convolution approach with attention-oriented BiLSTM is applied. To test the validity of the performance of the proposed model, standard metrics were applied. The outcome indicates that the suggested model achieves a significantly improved accuracy of 96.56%, superior to other models.
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.
With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%.
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