Real Time Sentiment Analysis On Twitter Data Using Deep Learning(Keras)

Kathuria, Ramandeep Singh; Gautam, Siddharth; Singh, Arjan; Khatri, Smarth; Yadav, Nishant

doi:10.1109/icccis48478.2019.8974557

Cited by 16 publications

(3 citation statements)

References 6 publications

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“…To prepare the input for deep learning mathematical models, preprocessing is done first. The protein sequences are tokenized into numbers using the Tokenizer function from the Keras ( Kathuria et al, 2019 ). This function first calculates the frequency of each character across all sequences, and then maps the top N characters with the highest frequency to the numbers 1 to N, the next N characters with the next highest frequency to the numbers N + 1 to 2 N, and so on.…”

Section: Methodsmentioning

confidence: 99%

ROSes-FINDER: a multi-task deep learning framework for accurate prediction of microorganism reactive oxygen species scavenging enzymes

Yan,

Shi,

Wei

2023

Front. Microbiol.

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Reactive oxygen species (ROS) are highly reactive molecules that play important roles in microbial biological processes. However, excessive accumulation of ROS can lead to oxidative stress and cellular damage. Microorganism have evolved a diverse suite of enzymes to mitigate the harmful effects of ROS. Accurate prediction of ROS scavenging enzymes classes (ROSes) is crucial for understanding the mechanisms of oxidative stress and developing strategies to combat related diseases. Nevertheless, the existing approaches for categorizing ROS-related proteins exhibit certain drawbacks with regards to their precision and inclusiveness. To address this, we propose a new multi-task deep learning framework called ROSes-FINDER. This framework integrates three component methods using a voting-based approach to predict multiple ROSes properties simultaneously. It can identify whether a given protein sequence is a ROSes and determine its type. The three component methods used in the framework are ROSes-CNN, which extracts raw sequence encoding features, ROSes-NN, which predicts protein functions based on sequence information, and ROSes-XGBoost, which performs functional classification using ensemble machine learning. Comprehensive experiments demonstrate the superior performance and robustness of our method. ROSes-FINDER is freely available at https://github.com/alienn233/ROSes-Finder for predicting ROSes classes.

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Section: Methodsmentioning

confidence: 99%

ROSes-FINDER: a multi-task deep learning framework for accurate prediction of microorganism reactive oxygen species scavenging enzymes

Yan,

Shi,

Wei

2023

Front. Microbiol.

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show abstract

“…Each text input is transformed into an integer sequence or a vector with a coefficient for each token in the form of binary values for this purpose. Keras Tokenizer has four methods, namely, fit_on_texts, texts_to_sequences, texts_to_matrix and sequences_to_matrix, (Tensorflow.org; Kathuria, Gautam, Singh, Khatri, & Yadav, 2019;Vinayakumar, Alazab, Jolfaei, Soman, & Poornachandran, 2019).…”

Section: Word Embedding/feature Vectormentioning

confidence: 99%

“…The texts_to_matrix method of the tokenizer class is useful for transforming a document into a NumPy matrix. The sequences are converted into NumPy matrix form using the Keras tokenizer class function sequences to matrix (Kathuria et al, 2019;Vinayakumar et al, 2019). This study used the Keras tokenizer class as a word embedding method for multiclass classification.…”

Section: Word Embedding/feature Vectormentioning

confidence: 99%