Applications and Challenges of Sentiment Analysis in Real-life Scenarios

Abstract

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.

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

“…For the imbalanced dataset, the training data derived from the initial step were further divided into Train and Validation sets at an 80:20 ratio. For the balanced dataset, the procedure entailed two phases: (1) The training data from the first stage were augmented with synthetic data to achieve a balanced state. (2) Upon reaching this balance, the synthetic data were split into Train and Validation sets, again with the 80:20 ratio.…”

“…As digital communication continues to expand, the ability of sentiment analysis to interpret complex human emotions and opinions becomes increasingly important, proving invaluable in fields ranging from social sciences to customer service and beyond. In this era of increasing digitization, leveraging the power of data through sentiment analysis offers unique insights, making significant contributions to sectors such as those previously summarized in various studies, namely, healthcare, social policy, e-commerce, and digital humanities [1]. In the year 2023, sentiment analysis experienced a significant surge in usage, employing advanced techniques to analyze diverse data sources.…”