Sentiment Analysis of Multilingual Mixed-Code, Twitter Data Using Machine Learning Approach

Swamy, Sowmya; Kundale, Jyoti; Jadhav, Dipti

doi:10.1007/978-981-16-2597-8_58

Cited by 3 publications

(1 citation statement)

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“…The recognition process of emotion was performed based on title, comments, and body where results in the emotions easily making wrong predictions as anger (Wang et al, 2021b;Alkhaldi et al, 2022). A knowledge-enabled sentiment analysis BERT model guides the input sentence embedding extracts data from the knowledge graph based on sentiment analysis and recognizes the related information to improve the performance of sentimental analysis (AlBadani et al, 2022;Swamy et al, 2022). Using multi-domain sentiment classification, a continuous naive Bayes framework for large-scale product evaluations on e-commerce sites can reduce computation for such reviews and enhance the capability of continuous learning from different domains (Neogi et al, 2021;Narwal and Aggarwal, 2022).…”

Section: Introductionmentioning

confidence: 99%

A dynamic weight function based BERT auto encoder for sentiment analysis

Prabhu,

Nashappa

2023

Int. J. Appl. Sci. Eng.

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Sentiment analysis is a crucial task in Natural Language Processing (NLP) that determines whether users have positive, neutral, or negative feelings about movies or products. NLP is being employed to address challenges related to the implementation of sentiment analysis. In these issues, basic polarity detection has been replaced by intricate emoticons that differentiate between various negative emotions. Utilizing a feature set to train the classifier, machine learning (ML) techniques can surpass lexicon-based methods in performance, but their effectiveness may be constrained to specific applications. The existing algorithms were processed to reduce the effects on social media and fake news detections but do not work on the classification and validation of tweets. To overcome these issues, a novel Deep Learning model with Weight Function based Bidirectional Encoder Representation from Transformers (WF-BERT) is proposed in this research to achieve high classification accuracy compared to the conventional ML techniques The weight functions of input and output elements in the proposed model are determined dynamically, based on the connection link between input and output. The obtained output results of the proposed model achieved an accuracy of 93.66 %, recall of 87.30 %, the precision of 88.00 %, and F1-score of 87.90 %. The proposed model outperforms existing language representation models with limited training data by including additional domain information.

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