Sentiment Analysis – Part I: Where It Is Used and Which Types Exist

Sentiment Analysis – Mining Emotions

From product feedback to sympathy ratings for politicians: Sentiment Analysis (SA) can be used to extract and analyze emotional preferences from text data. How do companies and political parties use this “Emotion AI” and what types of SA are used?

by Rashed Sabra, C++ Developer at L-One

Over the past 10 years huge amounts of data have been generated on the internet. This data is readily available in several formats including text, sound, and pictures.

The textual data can be gathered everywhere: from feedbacks users have given on products, tweeted statements, social media statuses and comments, news articles, emails, SMSs, chat rooms, information on web pages, video channels and many many more.
On Twitter for example, the number of Tweets per minute has increased by 58% for example since 2013 to more than 474,000 Tweets per minute in 2019!

Based on this data, Sentiment Analysis can provide deep insight into social behavior or sentiments on specific topics.

Sentiment Analysis is an area of NLP

Sentiment Analysis (SA), the process of mining meaningful patterns from text data, is one of the most important fields of NLP. SA includes interpreting and classifying emotions (positive, negative, and neutral) within text data using text analysis techniques.

What is NLP?

When the input data is mostly available as natural human language, such as free text, the procedure is defined as Natural Language Processing (NLP).

Sentiment Analysis is used, for example, by companies. From a business point of view, by automatically analyzing customer feedback from survey responses to social media conversations, brands can “listen” to their clients. This means, they automatically gather their customers’ comments, and adjust product features accordingly.

»With the massive usage of digital platforms for expressing opinions nowadays, Sentiment Analysis allows us a deep insight into the collective behavior of society or on an issue, be it a product performance or even a candidate in an election.«
Rashed Sabra, C++ Developer at L-One

Sentiment data can help us understand the current and historical context of an issue. With automated analytics, we can grasp what the wider audience thinks about an event, product or concept faster than ever before and take appropriate action. We can quickly understand what the wider audience is thinking about an event, product and concept and take the actions accordingly.

The information that is stored in the text allows computing an indicator such as negative, neutral, or positive. This indicator can serve as a signal for decision-makers.
Governments for instance have used Sentiment Analysis results during their election campaigns.

Reasons for Caring About Sentiment Analysis

In a commercial context, SA can provide online advice and recommendations for both the customers and merchants. On the one hand, user preferences that the data reveals can be used to help e-commerce platforms analyzing their products and services. On the other hand, for the virtual nature of online shopping, it is not easy to understand a commodity comprehensively and objectively, and whether the consumer is willing to learn about other consumers’ comments or opinions.

The massive demand for political information can be regarded as another important factor driving SA applications. In the analysis of the conversation on Twitter before the European Parliament elections, researchers have collected more than 1.2 million tweets in three languages (English, German, and French) during a two-week period (May 1 to May 14, 2014).

Public security, sociopolitical events such as the Arab Spring and the London Riots vividly demonstrate the importance of sentiment analysis to public security.

»How do we look at Sentiment Analysis projects at L-One Systems? We find the right research setup to generate the solutions that our clients need to solve their individual problems.«
Sarah Holschneider, Head of NLP at L-One

To sum up, Sentiment Analysis is important not only for traditional consumers and companies conducting surveys to gather opinions about corresponding products or services, but also it plays an important role on national security and public opinion analysis.

Types of Sentiment Analysis

SA can occur in various forms, from models that focus on polarity (positive, negative, neutral), to those that detect feelings and emotions (angry, happy, sad, … ), or even models that identify intentions (e.g. interested vs. not interested).

Some of the most popular types of Sentiment Analysis are Fine-Grained Sentiment Analysis, Emotion detection, and Aspect-based Sentiment Analysis.

Fine-grained Sentiment Analysis

If polarity precision is important to your business, you might consider expanding your polarity categories to include:

This is usually referred to as fine-grained sentiment analysis, and could be used to interpret 5-star ratings in a review, for example:

• Very Positive = 5 stars
• Very Negative = 1 star

A fine-grained analysis can provide more precise results to an automated system that prioritizes addressing customer complaints. In addition, dual-polarity sentences such as “The location was truly disgusting … but the people there were glorious.” can confuse binary sentiment classifiers, leading to incorrect class predictions.

Emotion detection

As the name suggests, emotion detection aims at detecting emotions, like happiness, frustration, anger, sadness, and so on. Many emotion detection systems use lexicons, lists of words and the emotions they convey or complex machine learning algorithms.

“I simply love it!” or “This movie scares me to death!” are good examples for emotional statements.

One of the downsides of using lexicons is that people express emotions in different ways. Some words that typically express anger, like bad or kill: “Your product is so bad!”, “Your customer support is killing me!”, might also express happiness : “This is bad ass support” or “You are killing it!”.

Aspect-based Sentiment Analysis

Usually, when analyzing sentiments of texts, let’s say product reviews, you’ll want to know which particular aspects or features people are mentioning in a positive, neutral, or negative way. That’s where aspect-based sentiment analysis can help. An aspect is an attribute or a component of an entity.
For example, the food was great but the service was awful.
The features: [Food, Services] for the entity: restaurant.

This problem involves subproblems such as identifying relevant entities, extracting their features or aspects, and determining whether an opinion expressed on each aspect or feature is positive, negative, or neutral. Basically, aspect-based analysis needs to find explicit aspect expressions, which usually are nouns and noun phrases from the text of a given domain. These nouns and other word classes can be identified by a part-of-speech tagger (POS).

Methods and Challenges

How does Sentiment Analysis work, which methods and algorithms are used, and which challenges do exist? All that, you can find out here – read on now.

About the author:
Rashed Sabra is currently finishing a master degree in Big Data Systems and researches in the area of Natural Language Processing. He holds a Bachelor degree of Artificial Intelligence. In 2020 he joined the L-One Team and since worked on a project developing a platform for a human-robot interface (HRI). In his spare time, he reads books and enjoys watching his favorite football team (FC Bayern Munich).