Abstract
Polarity prediction is the field of study that discovers people’s opinions, feelings, assessments, perspectives and feelings about associations and their attributes as communicated in ...written text. It is one of the most active research areas in the field of text mining. Nowadays online reviews play an important role by giving a helping hand to the customers to know about other customer’s opinions about the product they are going to purchase. This also guides the organizations and government sectors to increase their quality of product and services. Pre-trained BERT (Bidirectional Encoder Representations from Transformers) is used for word embedding in this model. The fine-tuned BERT is used for better word representation which in turn improves the sentimental analysis classification accuracy. Bidirectional Long Short-Term Memory classifier is utilized for polarity prediction. To enhance the performance of Bidirectional Long Short-Term Memory, the weight parameters of Bi-directional LSTM are optimally selected by using APSO algorithm. Improved self-attention mechanism is added with BiLSTM for focusing on significant words in the context. For performance analysis, four bench mark datasets are used for experiments.
Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online ...retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.
Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and ...behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.
Nowadays online reviews play an important role by giving an helping hand to the customers to know about other customer’s opinions about the product they are going to purchase. This also guides the ...organizations as well as government sectors to increase their quality of product and services. So automatic review summarization becomes more important rather than summarizing it manually as it saves time. The aim of this work is to produce a comprehensive summary which includes all key content from the source text. The Proposed Automatic Review Summarization model with improved attention mechanism increases the semantic knowledge and thus improves the summary’s eminence. This encoder-decoder model aims to generate summary in an abstractive way. The Pointer generator mechanism solves the problem of rare words which are out-of-vocabulary and the repetition issues are overcome by coverage mechanism. Experiments were conducted on Amazon’s mobile reviews dataset reveals that the proposed methodology generated more accurate abstractive review summarization when compared with existing techniques. The performance of the summary report is measured using the evaluation metric ROUGE.
Wireless Sensor Networks (WSNs) are extensively utilized across diverse applications, ranging from environmental monitoring to industrial automation. These networks deploy autonomous devices equipped ...with sensors to monitor environmental conditions such as temperature, humidity, and light. They play crucial roles in fields like precision agriculture, healthcare monitoring, and smart cities infrastructure. In the domain of WSNs, ensuring both security and precise localization is vital for sustaining optimal network performance and facilitating timely event detection. Despite numerous localization studies, existing approaches often fall short in 3D localization due to low beacon node accuracy and sparse environment coverage. Furthermore, achieving higher localization accuracy is challenging due to potential attacks that can compromise network integrity and reduce battery life. To address these issues, a novel Secure 3D algorithm is introduced in this paper to combat malicious beacon nodes as well as to enhance localization accuracy. The proposed Secure 3D algorithm demonstrates superior performance compared to the 3D DV-Hop, 3D APIT and Improved 3D localization algorithms in terms of Average Localization Error (ALE), Bad Node Proportion (BNP), and Localized Node Proportion (LNP), while also being energy-efficient. This algorithm achieves an ALE ranging from 3.0 to 6.5, a BNP below 0.2, and an LNP exceeding 0.8, all while consuming 100–130mW of energy. By addressing the challenges of malicious beacon nodes and enhancing localization accuracy, the proposed work not only improves network reliability but also ensures energy-efficient energy localization, making it a promising solution for enhancing the performance and security of WSNs in 3D environments.
Abstract
Information of quick texts is crucial for many packages however there are many demanding situations. First, the fast texts do not constantly look at a written language’s syntax. As a end ...result, traditional natural language processing gear cannot be without problems applied, ranging from element-of-talk tagging to dependency parsing. second, brief texts usually do now not incorporate sufficient statistical signals to help a few of the state of play Thirdly, short texts are greater ambiguous land noisier, land are generated in a big quantity, which further will increase the problem of coping with them. We argue that semantic knowledge is important for better understanding of brief texts. on this paintings, we build a prototype gadget for short text expertise that takes advantage of semantic expertise furnished by using a famous know-how base and harvests it automatically Our methods for responsibilities which includes text segmentation, component-of-speech tagging, and idea labeling, inside the feel that during these kind of duties we awareness on semantic. We perform a complete evaluation of the overall performance on actual-existence facts. The outcomes display that semantic information is critical for the understanding of quick texts and our information-intensive technique.
Businesses in the E-Commerce sector, especially those in the business-to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while ...keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. The best course of action in this circumstance is to detect prospective churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Each customer's information, including searches made, purchases made, frequency of purchases, reviews left, feedback is given, and other data, is kept on file by the e-commerce company. Machine learning and data mining may be aided by examining this enormous quantity of data, analysing customer behaviour, and seeing potential attrition opportunities. The support vector machine is a popular supervised learning method in machine learning applications. Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-Commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle.
Abstract
The social media can be a platform for information consumption nowadays. On the one hand, it’s free of cost, easy access, and different data dissemination lead people to hunt out and consume ...social media news. On the contrary, it allows for the broad spread of “spams,” i.e., inferiority news with deliberately false information. The widespread spread of spams has the potential for very negative impacts on people and society. Consequently, the detection of spam on social media has recently become an important research that draws tremendous attention. NLP, an artificial intelligence (AI) division, uses computers and human natural language to produce useful data. In text classification activities, such as spam detection and sentiment analysis, text generation, language translations and document classification, NLP is widely used.
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