# OSN Dashboard Tool For Sentiment Analysis

arXiv: https://arxiv.org/abs/2206.06935 · HTML: https://mlsanity.com/paper/2206.06935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 62 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Sentiment Polarity Detection for Software Development](https://mlsanity.com/paper/1709.02984/) (2017)
- [Predicting the Effects of News Sentiments on the Stock Market](https://mlsanity.com/paper/1812.04199/) (2018)
- [Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning -- a Case Study on COVID-19](https://mlsanity.com/paper/2008.10031/) (2020)
- [Sentiment Analysis for Education with R: packages, methods and practical applications](https://mlsanity.com/paper/2005.12840/) (2020)

## Abstract (arXiv)

The amount of opinionated data on the internet is rapidly increasing. More and more people are sharing their ideas and opinions in reviews, discussion forums, microblogs and general social media. As opinions are central in all human activities, sentiment analysis has been applied to gain insights in this type of data. There are proposed several approaches for sentiment classification. The major drawback is the lack of standardized solutions for classification and high-level visualization. In this study, a sentiment analyzer dashboard for online social networking analysis is proposed. This, to enable people gaining insights in topics interesting to them. The tool allows users to run the desired sentiment analysis algorithm in the dashboard. In addition to providing several visualization types, the dashboard facilitates raw data results from the sentiment classification which can be downloaded for further analysis.