Types of sentiment analysis and their applications in Business

Introduction

Sentiment analysis is a text classification technique that identifies and extracts data from the source material, allowing data analysts to gain a deeper understanding of the social perception surrounding their product or service while monitoring online chats.

In this article, we will define sentiment analysis as well as the various types and methods available.

We will also give a better understanding of sentiment analysis use cases in business, as well as its relationship with natural language processing and machine learning.

So, if you have any questions about this technology, continue reading.

What is sentiment analysis?

It is the process of determining whether a written piece is positive, negative, or neutral. A text sentiment analysis system uses natural language processing and machine learning algorithms to assign measured sentiment scores to entities, subjects, thematics, and categories within a phrase or sentence.

I — Types of Sentiment Analysis

1- Emotion Recognition

Emotion detection aids in the detection of emotions such as rage, unhappiness, joy, anger, anxiety, concern, distress, and others.

Emotion detection systems typically use lexicons, which are collections of words that express specific emotions; however, advanced classifiers and powerful machine learning algorithms are also used.

ML is preferred over lexicons because people express their emotions in a variety of ways.

2- Fine-Grained

Analysts can use this sentiment analysis model to determine polarity precision. A sentiment analysis can be conducted across the following polarities: very positive, positive, neutral, negative, or very negative.

Fine-grained sentiment analysis can help with review and rating analysis. Consider 1 as extremely negative and 5 as extremely positive on a scale of 1 to 5. On a scale of 1 to 10, 1–2 is very negative, while 9–10 is very positive.

3- Aspect-Based

In determining the overall polarity of customer reviews, aspect-based analysis goes deeper than fine-grained analysis. It aids data analysts in determining the specific topics that individuals are discussing.

4- Intent Analysis

By correctly determining consumer intent, businesses can save time, money, and effort.

The intent analysis helps them determine whether the customer intends to buy or simply browse. If a customer expresses an interest in purchasing, they can be tracked and targeted with advertisements.

5- Entity sentiment Analysis

Entity Sentiment Analysis merges entity and sentiment analysis to identify the sentiment expressed in a text.

Each mention of an entity is represented numerically by a score and magnitude value.

These scores are then gathered to produce an entity’s overall sentiment score and magnitude.

II — Categories of Sentiment Analysis

1- Knowledge based

This method included categorizing text based on emotional words.

Such a methodology is commonly proposed for sentiment analysis in social networks. It typically focuses on semantic processing while taking content into account and dealing with public user opinions as knowledge excerpts.

The method makes use of knowledge graphs, similarity measures, graph theory algorithms, and a disambiguation process.

2- Knowledge based

For accurate sentiment detection, this approach employs machine learning algorithms such as latent semantic analysis and deep learning.

3- Hybrid

The hybrid approach of sentiment analysis employs both statistical and knowledge-based methods for detecting polarity. It benefits from machine learning’s high accuracy from the statistical methods and the stability from the lexicon-based approach.

III — Sentiment Analysis in Machine Learning & Natural language processing

Building a sentiment analysis platform necessitates in-house expertise and large training data sets, which is typically the case when a company or business has unique requirements that are not met by existing platforms.

In such cases, businesses typically create their own tools using open source libraries. HuggingFace, SpaCy, Flair, and AllenNLP are NLP libraries that can perform sentiment analysis, and other machine language tools, such as PyCaret and Fast. AI also support sentiment analysis.

Deep learning transformer models such as BERT and XLNet can be used for sentiment analysis, while GPT3 analyzes sentiment without any training data.

IV — Sentiment Analysis Datasets

Large training data sets are required for ML and deep learning approaches to sentiment analysis.

Although available tools frequently have large databases, they are frequently very generic and not specific to specific industry domains.

As a result, given enough time, companies with specific needs can collect their own datasets.

A new or small business with a limited set of domain-specific training data can rely on a standard tool and tailor it to its specific requirements.

V — Use of Sentiment Analysis

Sentiment analysis assists data analysts in evaluating public opinion, conducting complex market research, monitoring brand and product public image, and attempting to understand customer engagement while providing useful insights to their own clients.

1 — Feedback & Customer Analysis :

In general, sentiment analysis works best when used as a tool for the voice of customers. Sentiment analysis is used by analysts, product managers, customer support directors, personnel management, and other relevant parties to understand how customers and workers feel about specific topics and why they have that feeling.

The Voice of the Customer is the process by which businesses hear and respond to customer feedback about their brand, products, and services.

These solutions assist them in gathering feedback and converting it into valuable data and insights at scale.

Because social media now controls brand images, a single viral review can destroy an entire business, whereas customers sharing positive experiences can increase revenue and customer lifecycle.

2 — Brands monitoring & market analysis :

Information shared about a brand through internet marketing, social media campaigns, and content marketing can be more important than the product or service itself.

One of the most crucial objectives of sentiment analysis is to gain a comprehensive understanding of what a business is and what the market requires.

It is possible to detect trends and predict outcomes using machine learning and sentiment analysis, allowing businesses to stay ahead of the competition.

They can also help them determine how well a brand is performing and what else they can do to increase sales. Analysts can also access the responses their competitors have provided. Based on the responses they’ve received, they can improve their game.

Conclusion

Artificial intelligence’s sentiment analysis is still in its early stages. Because of the complexities of human communication, the field has a lot of room for improvement. However, the techniques used in sentiment analysis models today appear promising and useful for many businesses.