Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM)

Twitter sentiment analysis is an automated process of analyzing the text data which determining the opinion or feeling of public tweets from the various fields. For example, in marketing field, political field huge number of tweets is posting with hash tags every moment via internet from one user to another user. This sentiment analysis is a challenging task for the researchers mainly to correct interpretation of context in which certain tweet words are difficult to evaluate what truly is negative and positive statement from the huge corpus of tweet data. This problem violates the integrity of the system and the user reliability can be significantly reduced. In this paper, we identify the each tweet word and we are assigning a meaning into it. The feature work is combined with tweet words, word2vec, stop words and integrated into the deep learning techniques of Convolution neural network model and Long short Term Memory, these algorithms can identify the pattern of stop word counts with its own strategy. Those two models are well trained and applied for IMDB dataset which contains 50,000 movie reviews. With huge amount of twitter data is processed for predicting the sentimental tweets for classification. With the proposed methodology, the samples are experimentally collected from the real-time environment can be discriminated well and the efficacy of the system is improved. The result of Deep Learning algorithms aims to rate the review tweets and also able to identify movie review with testing accuracy as 87.74% and 88.02%

Traffic scheduling, network slicing and virtualization based on deep reinforcement learning

The revolutionary paradigm of the 5 G network slicing introduces promising market possibilities through multi-tenancy support. Customized slices might be provided to other tenants at a different price as an emerging company to operators. Network slicing is difficult to deliver higher performance and cost-effective facilities through render resources utilisation in alignment with customer activity. Therefore, this paper, Deep Reinforcement Learning-based Traffic Scheduling Model (DRLTSM), has been proposed to interact with the environment by searching for new alternative actions and reinforcement patterns believed to encourage outcomes. The DRL for network slicing situations addresses power control and core network slicing and priority-based sizing involves radio resource. This paper aims to develop three main network slicing blocks i) traffic analysis and network slice forecasting, (ii) network slice admission management decisions, and (iii) adaptive load prediction corrections based on calculated deviations; Our findings suggest very significant possible improvements show that DRLTSM is dramatically improving its efficiency rate to 97.32%, scalability and compatibility in comparison with its baseline