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

Gandhi, Usha Devi; Kumar, Priyan Malarvizhi; Babu, Gokulnath Chandra; Karthick, G. S.

doi:10.1007/s11277-021-08580-3

Cited by 75 publications

(32 citation statements)

References 17 publications

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“…They were applied to the kNN approach and obtained 94% accuracy in attack detection. Gandhi et al 10 proposed the cuttlefish algorithm and Decision Tree algorithm to classify the attacks. The cuttlefish algorithm is used to reduce the feature space and the decision tree is used for classification.…”

Section: Related Workmentioning

confidence: 99%

Retracted: An empirical intrusion detection system based on XGBoost and bidirectional long‐short term model for 5G and other telecommunication technologies

Karthikraja¹,

Senthilkumar²,

Rajadurai

et al. 2022

Computational Intelligence

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Recently, intrusion detection system (IDS) has become an essential remedy to protect networks from malicious activities and attacks. The attacks force the network activities under risk and cause network data as an unrecoverable one. The attacks have different features in nature and their detection rate is low. An efficient attack detection system requires a huge volume of network transactions and relevant features for training. But the existing datasets have significant and nonsignificant features together. Hence, the significant feature selection plays a major role to detect the attacks. For this reason, this paper adopts the XGBoost approach to identify the relevant (significant) features of the attacks in the dataset. After this XGBoost, this article applies the bidirectional long short term model (Bi-LSTM) for the detection and classification of the attacks. This Bi-LSTM model is a novel approach to the IDS and very effective to increase the detection rate. Thus, this article focuses on feature reduction and classification of the attacks.The experimental results show that the proposed 1216

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Section: Related Workmentioning

confidence: 99%

Retracted: An empirical intrusion detection system based on XGBoost and bidirectional long‐short term model for 5G and other telecommunication technologies

Karthikraja¹,

Senthilkumar²,

Rajadurai

et al. 2022

Computational Intelligence

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“…The supervised sentiment analysis approaches developed by previous research range from traditional machine learning (ML) [27,28] to deep learning (DL) [29][30][31]. However, more focus has been given to DL-based methods in recent research, considering their improved performance over traditional ML-based approaches [10,32].…”

Section: Modelsmentioning

confidence: 99%

TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments

Hettiarachchi

Al-Turkey

Adedoyin-Olowe

et al. 2022

Data

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Even though social media contain rich information on events and public opinions, it is impractical to manually filter this information due to data’s vast generation and dynamicity. Thus, automated extraction mechanisms are invaluable to the community. We need real data with ground truth labels to build/evaluate such systems. Still, to the best of our knowledge, no available social media dataset covers continuous periods with event and sentiment labels together except for events or sentiments. Datasets without time gaps are huge due to high data generation and require extensive effort for manual labelling. Different approaches, ranging from unsupervised to supervised, have been proposed by previous research targeting such datasets. However, their generic nature mainly fails to capture event-specific sentiment expressions, making them inappropriate for labelling event sentiments. Filling this gap, we propose a novel data annotation approach in this paper involving several neural networks. Our approach outperforms the commonly used sentiment annotation models such as VADER and TextBlob. Also, it generates probability values for all sentiment categories besides providing a single category per tweet, supporting aggregated sentiment analyses. Using this approach, we annotate and release a dataset named TED-S, covering two diverse domains, sports and politics. TED-S has complete subsets of Twitter data streams with both sub-event and sentiment labels, providing the ability to support event sentiment-based research.

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“…Typical categories can be positive, neutral, or negative sentiment, reflecting a higher satisfaction, none, or dissatisfaction of the subject related to the studied variable [11], [12]. There are many applications using sentiment analyses techniques in the social domain, including the recommendation systems [13], movie reviews [14], identification of cyber-aggression [15], racism [16] and violence against women [17].…”

Section: A Sentiment Analysismentioning

confidence: 99%

Approval Rating of Peruvian Politicians and Policies using Sentiment Analysis on Twitter

Vidalón¹,

Solis²,

Porras³

et al. 2022

IJACSA

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Nowadays, using the social network Twitter, a subject can easily access, post, and share information about news, events, and incidents taking place currently in the world. Recently, due to the high number of users and the capability to transfer information instantly, Twitter had attracted the interest of politicians with the goal to interact with their followers and to communicate theirs polices. Fearing the disagreements and disturbances that the application of some policies might cause, usually politicians use surveys to support their actions. However, such studies still use traditional questionnaires to recover information and are costly and time-consuming. Recent advances in automatic natural language processing have allowed the extraction of information from textual data, like tweets. In this work, we present a method to analyze Twitter data related to Peruvian politicians and able to score the latent sentiment polarity of such messages. Our proposal is based on an embedding representation of tweets, which are classified by a convolutional neural network. For evaluation, we collected a new dataset related to the current President of Peru, where the model achieved 91.2% of sensibility and 94.4% of specificity. Furthermore, we evaluated the model in two politic topics, that were totally unknown for the model. In all of them, our approach gives comparable results to renowned Peruvian pollsters.

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

Cited by 75 publications

References 17 publications

Retracted: An empirical intrusion detection system based on XGBoost and bidirectional long‐short term model for 5G and other telecommunication technologies

Retracted: An empirical intrusion detection system based on XGBoost and bidirectional long‐short term model for 5G and other telecommunication technologies

TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments

Approval Rating of Peruvian Politicians and Policies using Sentiment Analysis on Twitter

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