Analyzing opinion of different countries for recent events in Afghanistan using text mining

Aggarwal, Sachin; Khan, Shoaib Showkat; Kakkar, Misha

doi:10.1109/confluence52989.2022.9734179

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this literature, research uses an instantaneous evaluation of social media user's emotional inclinations and grades the emergency at various stages of social media user's emotions and propagation in order to comprehend the disaster threat. In [30] author proposed an opinion analysis of the tweets to utilize analogous words while tweets have identical positive and negative words to reflect the association of emotional oppressors paying little responsiveness to deliberate the different occasions in Afghanistan. Social media has acquired ubiquity for user communication and data inclination.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

Suman,

Varshney,

Chouhan

et al. 2024

IJCDS

View full text Add to dashboard Cite

X (formerly Twitter) has become a vital source of information on various variety of social, political, and economic concerns, as a consequence of its growth and popularity which has resulted in an enormous number of people sharing their opinions on a wide range of areas. To determine people's emotions about the Russia-Ukraine war (RUW), this study examines trends in English-language tweets. In this work, we have engaged 34 countries to tweet opinions that produce a strong perception of the people about the war and message to the world what people famine from the countries and that affects their lives. To analyze positive and negative emotions in tweets, which are represented by hope and fear, the LSTM-CNN model is based on deep learning. A time series is calculated that correlates with the rate of recurrence of negative and positive tweets in different nations. Additionally, an approach based on the average of the neighborhood has been used for modelling and grouping the time series of various countries. The clustering method gives results as significant information, how people feel about this dispute and share their opinions about RUW is approached. When compare on different models on overall data that the 96% accuracy is Achieved by the LSTM-CNN model. 97.09% accuracy, is achieved when the comparing the tweets from the cluster 1 countries. When comparing the tweets from the cluster 2 countries the 99% accuracy, is achieved. 97% accuracy is achieved by comparing the tweets from cluster 3 countries. 97% accuracy, is achieved when the comparing the tweets from cluster 4 countries. 96% accuracy is achieved by the LSTM-CNN model when the comparing the tweets from cluster 5 countries by the different models. This research study helps the uninfluenced press members to have an impartial source of information for their reports and articles.

show abstract

Section: Literature Surveymentioning

confidence: 99%