Polarity Detection Using Digital Media

Bharathan, Kavya; Varma, P Deepasree

doi:10.1109/icacc48162.2019.8986172

Cited by 1 publication

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some works have been done by Islam et al [14] on online news articles in English, Bharathan, and Varma [15] on digital media in English, Pivovarova et al [18] on business English news articles, Ahmad et al [28] on Bengai News articles.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Bengali Newspaper Headline Polarity Using Machine Learning Techniques

Barua

Raza

Islam

et al. 2023

Preprint

View full text Add to dashboard Cite

Automated detection of the sentiment of newspaper headlines can save time and effort in reading and help to create a positive environment by reducing the prevalence of negative news and promoting positive news. Negative news can have a variety of effects on society, including Increased fear and anxiety, Desensitization to violence and tragedy, Decreased trust in institutions, Polarization, and division, and Decreased well-being. In this study, we propose a system for detecting the positivity or negativity of newspaper headlines in Bangla. While there has been a significant amount of research on detecting the sentiment of English newspaper headlines, there has been none in Bangla. To build our dataset, we used a large number of Bangla newspaper headlines. To categorize these Bangla newspaper headlines, three classic machine learning models, e.g. support vector machine (SVM), logistic regression, and random forest, and one deep learning model, e.g. sequential model are utilized. We then applied these three classical machine learning models as well as a deep learning model to the dataset following a machine learning approach. The obtained results are quantified in terms of four inquisitive performance metrics – accuracy, F1-score, precision, and recall. The results showed that the sequential deep learning model surpassed all other classical machine learning models achieving an accuracy of 80% F1-Score of 79% Precision of 87% and Recall of 76%.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Bharathan and Varma [15] developed and model to automatically extract sentiments and summarize large documents as well. A rule-based sentiment analysis tool is used to extract sentiments from 10000 Twitter and news feed data.…”

Section: Related Work and Datasetsmentioning

confidence: 99%