# Applications and Challenges of Sentiment Analysis in Real-life Scenarios

arXiv: https://arxiv.org/abs/2301.09912 · HTML: https://mlsanity.com/paper/2301.09912/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 124 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 22 of 22)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Visualizing and Understanding Neural Models in NLP](https://mlsanity.com/paper/1506.01066/) (2015)
- [Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://mlsanity.com/paper/2005.06676/) (2020)
- [Explaining Recurrent Neural Network Predictions in Sentiment Analysis](https://mlsanity.com/paper/1706.07206/) (2017)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks](https://mlsanity.com/paper/1808.09238/) (2018)
- [It's not a Non-Issue: Negation as a Source of Error in Machine Translation](https://mlsanity.com/paper/2010.05432/) (2020)
- [Emotion Dynamics in Movie Dialogues](https://mlsanity.com/paper/2103.01345/) (2021)
- [Measuring Emotions in the COVID-19 Real World Worry Dataset](https://mlsanity.com/paper/2004.04225/) (2020)
- [A Survey on Sentiment and Emotion Analysis for Computational Literary Studies](https://mlsanity.com/paper/1808.03137/) (2018)
- [Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic](https://mlsanity.com/paper/2008.12172/) (2020)
- [Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews](https://mlsanity.com/paper/2010.13814/) (2020)
- [Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective](https://mlsanity.com/paper/1903.05801/) (2019)
- [Revisiting Negation in Neural Machine Translation](https://mlsanity.com/paper/2107.12203/) (2021)
- [Mobilizing the Trump Train: Understanding Collective Action in a Political Trolling Community](https://mlsanity.com/paper/1806.00429/) (2018)
- [Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation](https://mlsanity.com/paper/2109.10859/) (2021)
- [Sentiment-Aware Measure (SAM) for Evaluating Sentiment Transfer by Machine Translation Systems](https://mlsanity.com/paper/2109.14895/) (2021)
- [What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State](https://mlsanity.com/paper/1704.03407/) (2017)
- [Recommendation Chart of Domains for Cross-Domain Sentiment Analysis:Findings of A 20 Domain Study](https://mlsanity.com/paper/2004.04478/) (2020)
- [Distance Based Source Domain Selection for Sentiment Classification](https://mlsanity.com/paper/1808.09271/) (2018)

## Built on by (most cited first, 2 of 2)

- [Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation](https://mlsanity.com/paper/2407.10091/) (2024)
- [Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models](https://mlsanity.com/paper/2607.07772/) (2026)

## Abstract (arXiv)

Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research. However, its applications to the real world are a driving force for research in SA. This chapter describes some of these applications and related challenges in real-life scenarios. In this chapter, we focus on five applications of SA: health, social policy, e-commerce, digital humanities and other areas of NLP. This chapter is intended to equip an NLP researcher with the `what', `why' and `how' of applications of SA: what is the application about, why it is important and challenging and how current research in SA deals with the application. We note that, while the use of deep learning techniques is a popular paradigm that spans these applications, challenges around privacy and selection bias of datasets is a recurring theme across several applications.