Sentimental analysis on social media data using R programming

Bhargava, Mandava Geetha; Rao, Duvvada Rajeswara

doi:10.14419/ijet.v7i2.31.13402

Cited by 11 publications

(2 citation statements)

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“…The work done by Ravendra singh at [4] says that sentimental analysis is a need for the organizations and companies and a requirement for the customers. The proposed work of this paper is mostly focused on cryptocurrencies like bitcoin and Litecoin [5]. This paper quotes that the depth convolution neural network for sentiment analysis on the tweets has been used and GLoVe has been used to classify the sentiment of text [6].…”

Section: Related Workmentioning

confidence: 99%

Sentimental Analysis using Neural Networks

Agnal*,

Baranwal,

Krishna

et al. 2019

IJITEE

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Sentimental Analysis is to learn about opinion of the consumer that analyzes the understanding of a person or crew of people about a product and present day trends. This task pursuits to learn about the thoughts in a text that leads to a sentimental evaluation by imposing an algorithm to classify opinion. The algorithm that has been used is applied the use of R Language. Social media is one of the biggest elements that we stay with. Twitter is one of the social media that is in the public eye. The undertaking intends to design a sentimental evaluation by using extracting a huge number of tweets. Sentimental Analysis is then employed on this data, which recognizes and distinguishes the wonderful and bad critiques from the impartial tones on the involved topic. This model even extracts the information from newspaper blogs. The data mentioned about any subject matter in the newspaper blogs is precise. The records retrieved from the blogs are additionally analyzed and opinion is acquired. Using this model we gain the most excellent notion.

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Section: Related Workmentioning

confidence: 99%

Sentimental Analysis using Neural Networks

Agnal*,

Baranwal,

Krishna

et al. 2019

IJITEE

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“…Racism is the belief that various groups of people have different behavioral features that correspond to hereditary characteristics, and that these groups may be divided based on race superiority [2]. Expressions that have the potential to inspire fury or violence are known as intoxicating language [3].…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis based offensive language identification system for code-mixed data

Shanmugavadivel¹,

Malliga²,

2023

ACE

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Hate speech identification is the process of categorising textual information into hate and non- hate speech with the goal of identifying hate speech's targeted features. The objective of this research work is to take the Dataset from FIRE 2021 shared task code mixed data that includes YouTube comments and Twitter conversations and to detect whether the messages represents the offensive or non-offensive category. To detect the offensive language sentences, various deep learning models like Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network, Gated Recurrent Unit, and hybrid model like Convolutional Neural Network with Bidirectional Long Short-Term Memory methods were utilised in this research work. The performance of all the mentioned models is evaluated using precision, recall, F1-score, and accuracy. Out of all the models, both LSTM and GRU models perform better with the accuracy of 0.81, precision of 0.85 and recall of 0.95.

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