Top Use Cases for Sentiment Analysis in NLP
Tags: nlp, analytics
Published: 2022-01-20 22:11:16

Recent advances in Deep Learning and Artificial Intelligence have pushed forward significant text analytic applications. One of these applications is Sentiment Analysis.

Sentiment Analysis is a subfield of Natural Language Processing, or NLP, and is often used to understand natural language, whether it is written text or transcription text via a [Speech-to-Text API](https://www.assemblyai.com/blog/the-top-free-speech-to-text-apis-and-open-source-engines/).

This post will first explain what Sentiment Analysis is before examining top use cases for Sentiment Analysis in NLP.

## What is Sentiment Analysis?

[What is Sentiment Analysis?](https://www.assemblyai.com/blog/best-apis-for-sentiment-analysis/) In NLP, Sentiment Analysis is the process of detecting emotions in a specific speech segment from a transcribed audio or video file. It is also sometimes referred to as sentiment mining or extraction. 

Deep Learning models are trained to perform Sentiment Analysis using open source data found in Twitter feeds, review sites (like IMDB or Rotten Tomatoes), and talk show or news transcripts.

The most common approach to train a Sentiment Analysis model is by using Sentiment Polarity. For each specified input, the model will output a number on a scale from -1 to 1, where:

-1 = negative
0 = neutral
1 = positive

For example, the model might output the number .89. Since this is closest to 1, the speech segment would be labeled “positive.”

Another training method is multiclass classification, where instead of a sliding scale, the model would output a probability that a speech segment is positive, neutral, or negative. Then, the sentiment with the highest probability would be the one that is classified.

Currently, more research needs to be done in order to expand these limited scales to a more human range of emotions, though some models do already attempt this with varying degrees of success. Once higher accuracy can be attained on this imitation-human scale of emotions, it would of course expand the utility of Sentiment Analysis further.

## Sentiment Analysis Use Cases

Still, Sentiment Analysis can be a significant analytical tool for companies to implement. 

Let’s look at how some industries are already applying Sentiment Analysis successfully. 

## Call Centers

Call centers use Sentiment Analysis to extract sentiments in agent and customer interactions across specific products, locations, times of day, and agents. Then, they can analyze trends and make adjustments to agent training, product descriptions, customer relations, and more.

## Video Meetings

Video meeting platforms use Sentiment Analysis to extract sentiments in conversations by meeting topic, length, or format.

## Podcasts and Broadcast Media

Podcasts and Broadcast Media use Sentiment Analysis to find trends in how listeners react to particular content. Do more positive oriented podcasts produce more positive commentary? Do podcasts or news segments with more negative commentary incite negative commentary, infighting, or hate speech? Advertisers can also use Sentiment Analysis to help better determine which podcasts would be the best fit for sponsorship. 

## Telemedicine

Telemedicine uses Sentiment Analysis to audit doctor-patient conversations and interactions to ensure positive outcomes. Then, the analyzed data can be used to retrain doctors, rethink delivery methods, or identify other trends.