Sentiment Analysis: Definition, Objectives and Tools

1. Sentiment Analysis – Defintion and Purpose
2. What different approaches are there?
3. Sentiment Analysis – Purpose and Challenge
4. In which business and application areas can sentiment analyses be used?
5. Popular Analysis Tools
6. Conclusion

With the change and development of the internet and the growing dominance of social media, the role of users also changed significantly. Supported by a focus on social structures and interactions on the net and the associated technological improvements in the area of communication exchange, users were able to increasingly shed the role of mere consumers. There's never been an easier time to share opinions and experiences about products, services, or everyday topics like politics or economics publicly on the internet. Be it forums, reviews, tweets or blogs - the popularity of social networks and microblogging continues to rise and leaves traces on the internet in the form of vast amounts of user-generated content. However, due to the sheer volume of data, manual evaluation is hardly feasible, so automated techniques are needed. A method that has established itself over time and is already being used in many areas and industries is the so-called sentiment analysis. You can find out exactly what this is, how and where you can use it, as well as everything else important, in this article.

Sentiment Analysis – Defintion and Purpose

Sentiment analysis, also known as opinion analysis, mood analysis or opinion mining is an analysis method based on statistical procedures, techniques of natural language processing (NLP), and machine learning (ML). It is a subfield of data mining, more precisely text mining, and is used to identify, extract, and evaluate opinions, feelings, and moods in textual content. The main goal is to reveal the emotional orientation of a text, by capturing the moods and subjective attitudes expressed there and classifying them as positive, negative, or neutral using sentiment clusters.

What different approaches are there?

There are two basic approaches to text classification that can generally be distinguished in conducting sentiment analyses: dictionary-based approaches and machine learning methods.

Dictionary-based approaches

As the name suggests, sentiment classification is done here using dictionaries (sentiment dictionary). The goal is to determine the orientation of the entire document by analyzing the semantic orientation of words or sentences that appear in a particular document. The dictionaries used therefore contain words whose respective polarity is known. These so-called opinion words can be marked negatively if they describe an undesirable condition such as "bad" or "not good", or positively if they express desirable conditions such as "great" or "good". These prefabricated dictionaries can contain not only individual words, but also entire sentences and even idioms, and can be specifically tailored to a field as a result. Furthermore, the accuracy of a dictionary-based sentiment analysis can be improved by linking the opinion words with sentiment scores, thereby differentiating them according to the strength of their orientation. For example, "bad" could be assigned a sentiment value of -0.5 and "awful" a score of -1 to indicate the strength of the negative emotion more clearly (Medhat, Hassan, & Korashy, 2014).

The creation of the dictionary

Generating dictionaries can be done entirely manually, but this is associated with a tremendous amount of time and is therefore rarely practiced. Instead, there are automated approaches for generating dictionaries, at the end of which a manual review and, if necessary, a correction of errors is performed. The automatic creation of dictionaries can be distinguished between two types, the purely dictionary-based and corpus-based approach:

The dictionary-based approach starts with the manual selection of some words that represent known moods and marks them as positive or negative. These initial words, also referred to as seed words, are automatically extended using algorithms from online dictionaries like WordNet or Thesaurus. This process repeats iteratively until no new synonyms or antonyms are found any more. A manual review of the created dictionary completes the process. A disadvantage of this approach is that it does not take into account context- or domain-related words.

The corpus-based approach on the other hand, aims to create a dictionary for a specific context in order to tailor the analysis to a specific field of expertise. This creates a link to a specific field of application. This is often decisive for successful sentiment detection, as Twitter (now X) comments about politics and economics are to be evaluated differently than reviews about vacation and hotel (D'Andrea, Ferri, Grifoni, & Guzzo, 2015).

Machine Learning Algorithms

In this approach, the sentiment classification is performed using statistical methods and machine learning algorithms. You can distinguish between two types of learning here: unsupervised learning and supervised learning.

Unsupervised Learning

In unsupervised learning, the algorithm works without instruction. There are no pre-classified documents used as a training data set. The algorithm is not trained before deployment and therefore has no prior opportunity to derive dependencies and patterns from marked labels. The best-known method from the field of unsupervised learning is cluster analysis.

Supervised Learning 

However, the application of supervised machine learning methods dominates in the field of sentiment analysis. This means that the algorithm is fed and trained with training data in the form of documents and texts before deployment. It is important that the training data sets contain known and marked target results, so-called labels. By linking input data and the corresponding results as output, the algorithm can recognize patterns and connections in advance and then apply these later when used on a new data set, the test data set. With supervised machine learning methods, at least two different data sets are therefore needed with the training and test data set. The training data set is of particular importance.

If it has weaknesses or inaccuracies, as far as the connection of document and marked result is concerned, the results of the validation by using the learning algorithm on the test data set will also be marked with weaknesses and inaccuracies. In short: Bad input delivers bad output.

Due to this enormous dependency, labeling the training documents with the necessary additional information (label process) must be assigned a particularly high priority in the application of supervised machine learning methods. The best-known machine learning algorithms include the Naive Bayes algorithm, the support vector machines, or the maximum-entropy method which have all found successful application in the area of sentiment analysis (Kharde & Sonawane, 2016).

Hybrid methods

If you look at the idea behind the different methods, you probably quickly ask yourself whether you can combine both approaches for a sentiment analysis. The answer is clear: Yes, you can!

Therefore, a third, hybrid approach can be derived. While this is not a standalone method, it is more of a combination of both methods described. Hybrid analyses are becoming increasingly popular in the field of opinion analysis. The basic idea here is to use the generated dictionaries to label the training data. The process of properly preparing training data sets and potentially tailoring them to a specific field is always associated with a lot of manual effort. Specially developed dictionaries can therefore not only increase efficiency and effectiveness but also positively influence the accuracy of the results when used correctly.

Sentiment Analysis – Purpose and Challenge

Emotions often set the main impulse for changes. These emotions are therefore somewhere in a huge amount of user-generated text. But not only the sheer volume of data makes a manual analysis almost impossible. Often, the mood is not obviously displayed in the text. They are not always recognizable at first glance, but can hide subtly in text deserts. Negations further complicate things. Sentiment analyses support you in this and are extremely important, because one thing is clear:

The evaluation of emotional data is not an optional task if you want to compete with the competition in the long run.

The correct and efficient analysis of user-generated data has enormous potential from a wide variety of perspectives. Providers of services or products can gain decisive competitive advantages by evaluating the data and improve their strategic planning, decision-making processes, and the quality of products and services. For example, highly customer-centered companies like Amazon could use the information obtained from the analysis of customer reviews to improve their insight into customer preferences. Based on this, profitable changes and adjustments such as personalized product recommendations or rankings based on product popularity can be implemented. But the evaluation of user-generated data is not only advantageous from a market research perspective, but also offers opportunities to generate benefits and added value in many other areas.

In which business and application areas can sentiment analyses be used?

There are numerous areas where sentiments are of central importance. Here are a few specific application areas and tips:

• Customer Feedback: Customer satisfaction is a top priority for many companies. It doesn't initially matter whether you're a provider of a product or a service. Opinions and criticisms are always there and you always have to improve yourself and your products and especially recognize negative trends and tendencies early on. Sentiment analyses not only help to recognize the general mood in a text, but can also assign certain opinions to a topic at the same time. As a result, targeted optimization can take place. A suitable example for this are hotel room reviews. By detailed analysis of the sentiments, you can find out the areas of the hotel rooms that are particularly positively highlighted, but also those aspects that are associated with negative opinion words and should therefore be improved with high priority.

• Reputation Management: Sentiment analyses can also play a decisive role in reputation management by giving companies insight into public perception. By monitoring online opinions, reviews, and social media, companies can determine the overall sentiment image in relation to their brand. Positive feedback can be actively used to strengthen the image, while negative statements serve as an early warning system to address problems and protect the reputation of the brand. With sentiment detection, you can recognize patterns early on and adapt your business strategy to current trends to avoid potential damage to your company.

• Risk Assessment in Finance: Another field of application for sentiment analyses is in finance, especially in risk assessment. By analyzing sentiment-related data, such as news articles, social media, and analysts' reports, financial institutions and investors can gain insights into the mood and expectations of the market. Positive or negative opinions can have effects on share prices, bonds, and other financial instruments. Early detection of market changes and potential risks is essential in this field, as this is invaluable for portfolio management and strategic decisions. On the whole, sentiment analyses can help you to better understand financial market volatility and manage risks more effectively.

• Political Analyses: High demand for discussion is well-known in politics. Political parties and governments can use sentiment analyses in the political environment in many ways. On the one hand, the mood of the electorate on current issues can be recorded, which is helpful in formulating political messages and campaigns as well as in creating election prognoses. In addition, sentiment analyses help in crisis management, as early reactions can be made to emerging controversies or negative opinions. Communication strategies can thus be improved so that they are better attuned to the concerns and needs of the voters. In general, these analysis can make a decisive contribution to better connecting politically active people with their electorate, communicating more effectively, and holistically adapting political strategies.

• Employee Satisfaction: But sentiment analyses are also useful within a company. Especially for large organizations with many employees, it is often difficult to fully capture and evaluate the pulse of the employees and their individual opinions and emotions. However, this is extremely important, as this provides insights into employee satisfaction. Internal opinion mining and the resulting results allow for the adaptation of initiatives, better internal communication, leadership development, evaluation of team dynamics, and early detection of problems and points of contention.

Popular Analysis Tools

There are many professional analysis tools on the market that can assist you in conducting sentiment analyses. These sentiment analysis tools have proven themselves in the past:

Hootsuite

Hootsuite is a comprehensive platform for social media monitoring and analyses. It provides functions for monitoring brands, topics, and trends on social media. Hootsuite Insights' analysis allows for classification of social media content as positive, negative, or neutral. The platform uses advanced algorithms to detect the sentiment behind the posts. Hootsuite offers a user-friendly interface and is designed to be accessible to individuals with no technical background.

Talkwalker

Talkwalker is a social listening and analysis platform that helps companies monitor real-time mentions of their brand, products, and competitors. Talkwalker's sentiment analysis evaluates texts and posts on social media, websites, and other online sources. It offers insights about the sentiment and opinions of the target audience. Ideal for both professional users and those without extensive programming knowledge.

HubSpot Marketing Hub

HubSpot Marketing Hub is an integrated marketing software that supports various marketing activities, including social media monitoring. While HubSpot primarily focuses on marketing and automation, it also offers features for sentiment analysis to keep track of the sentiment of the audience on social media.

Sprinklr

Sprinklr is a comprehensive social media management platform that allows businesses to manage their social media presence, plan, publish, monitor, and analyze content. Within Sprinklr, there are various analysis and reporting functions that include sentiment analyses as well.

R-Studio

R-Studio in this sense is not a finished tool with an optimized user interface, but a very popular programming language in the field of data analysis and machine learning, including sentiment analyses, and should therefore definitely be mentioned. There are several R packages and libraries that have been specifically developed for sentiment detection and offer a wide range of functions. The advantage of R-Studio is also that there's a strong community offering support. An integrated development environment with a graphical user interface is, for example, R-Studio.

However, those who want to carry out sentiment analyses without programming knowledge and a technical background do not need to worry, but should rather take a closer look at the tools mentioned before.

Conclusion

Sentiment analyses can be used in many ways and in numerous application areas and industries, they are already indispensable today in order to be able to continue to successfully participate in competition in the medium term. While purely dictionary-based methodologies are used less often, an increasing number of sentiment detection studies are based on automated machine learning algorithms. The field of AI and machine learning is currently experiencing strong growth. It will be exciting to see how developments and AI trends affect opinion mining. In the meantime, there are already some software tools that have become established as supportive tools in sentiment marketing. Each tool has its own strengths and special features. As with any software, it depends on the needs, requirements, and resources of the users and, of course, their individual preferences. All platforms make it easier to monitor and analyze social media content in order to gain insights into audience opinions, moods, and trends. They help companies manage their brand reputation, understand customer feedback, and adjust their marketing strategies.