Political Sentiment Analysis: Case Study of Haryana Using Machine Learning

Yadav, Dharminder; Sharma, Avinash; Ahmad, Sharik; Chandra, Umesh

doi:10.1007/978-981-16-7018-3_36

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yadav et al [13] purposes for predicting the outcome of vote from Haryana in the tweet written in the English language. It can be utilized the Twitter Archiving Google Sheet (TAGS) tool and Twitter API utilizing R for obtaining the tweet.…”

Section: Introductionmentioning

confidence: 99%

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Alotaibi¹,

Alabdulkreem²,

Althahabi³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process. The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data. In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process. Besides, BLSTM model is employed for the effectual detection and classification of opinions. Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work. A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

show abstract

Section: Introductionmentioning

confidence: 99%