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

2021 ◽  
Author(s):  
Usha Devi Gandhi ◽  
Priyan Malarvizhi Kumar ◽  
Gokulnath Chandra Babu ◽  
Gayathri Karthick
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Twitter Data ◽  
Long Short Term Memory

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

2021 ◽  
Author(s):  
Usha Devi G ◽  
Priyan M K ◽  
Gokulnath Chandra Babu ◽  
Gayathri Karthick
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Twitter Data ◽  
Learning Techniques ◽  
Stop Word ◽  
Long Short Term Memory

Abstract 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%.

Sentiment analysis of tweets using a unified convolutional neural network‐long short‐term memory network model

2020 ◽  
Author(s):  
Muhammad Umer ◽  
Imran Ashraf ◽  
Arif Mehmood ◽  
Saru Kumari ◽  
Saleem Ullah ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Network Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning

2021 ◽  
Vol 35 (1) ◽  
pp. 63-70
Author(s):  
Siyin Luo ◽  
Youjian Gu ◽  
Xingxing Yao ◽  
Wei Fan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Text Sentiment Analysis

In view of the fact that a single sentiment classification model may be unstable in classification, this paper attempts to propose a joint neural network and ensemble learning sentiment analysis method. After data preprocessing such as word segmentation on the text, combined with document vectorization method for feature extraction, we then use four basic classifiers including long short-term memory network, convolutional neural network, a serial model combining convolutional neural network and long short-term memory network, and support vector machine to train model, respectively. Finally, the integration is carried out by stacking ensemble learning. The experimental results show that the integrated model significantly improves the accuracy of text sentiment analysis and it can effectively predict the sentiment polarity of the text.

scholarly journals Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1953
Author(s):  
Seyed Majid Azimi ◽  
Maximilian Kraus ◽  
Reza Bahmanyar ◽  
Peter Reinartz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Object Tracking ◽  
Short Term Memory ◽  
Aerial Imagery ◽  
Future Research ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.

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