Sentiment analysis: How to decode emotions from texts

With the Sentiment Analysis you can analyze and understand the opinions and emotions in text data. Whether in market research, marketing, customer service or political analysis, sentiment analysis offers companies and organizations valuable insights that were previously difficult to access.

With an ever-increasing amount of text data on social media, review platforms and customer service chats, the opportunities for sentiment analysis are also becoming more diverse and exciting.

All the more reason to take a closer look at this technology and the way in which you interpret data and opinions.

sentiment analysis definition

Sentiment Analysis: Definition

Sentiment Analysisalso referred to as opinion mining, is a NLP-A method for analyzing and quantifying opinions, emotions and moods in text data. It aims to capture the emotional content of a text and divide it into positive, negative or neutral evaluations. 

This is usually done with the help of machine learning and lexicon-based approaches. 

Sentiment analysis is used in many areas, including:

  • Social media monitoring
  • Market research
  • Customer feedback analysis
  • Product reviews

It can also be used for real-time monitoring of trends and public opinion. 

The main tasks include:

  • Text preprocessing: Text preprocessing refers to the process of preparing text data for analysis. This includes steps such as text cleansing, tokenization, removal of stop words and special characters, and conversion into a standardized form to reduce noise and prepare the data for further processing.
  • Feature extraction: Feature extraction is the process of extracting relevant information or features from the pre-processed text data. These can be words, phrases, statistical key figures or other features that are used to analyze and classify the sentiment.
  • Classification of sentiment: Sentiment classification is the step in which sentiment analysis is performed to determine the emotional content of the text. This is usually done by assigning the text to categories such as positive, negative or neutral, based on the extracted features and the classification rules defined in machine learning models.

The accuracy of sentiment analysis models depends on the quality of the training data and the model architecture. High-quality training data that covers a broad and representative range of opinions and emotions is crucial to develop a powerful model. 

Sentiment detection

Sentiment detection is a sub-area of sentiment analysis. 

It is the process of automatically identifying and classifying the sentiment or mood in texts. The aim of sentiment detection is to determine whether the emotional content of a text is positive, negative, neutral or even mixed.

Typically, sentiment detection is based on text data such as customer ratings, social media posts, product reviews, comments and other written content. It takes place at various levels of text processing, from the document level to the sentence or word level.

The results of sentiment detection are often divided into categories such as "positive," "negative," "neutral" or into finer gradations, depending on the requirements of the use case. 

The accuracy of sentiment detection depends on the quality of the training data, the model architecture and the adaptation to the specific text context.

sentiment analysis function

Functionality

Sentiment analysis uses machine learning and natural language processing. 

Below you will find a rough description of how it works:

  1. Data acquisition

    First, text data is collected from various sources, e.g. social media posts, product reviews, customer surveys or texts from other text sources.

  2. Text preprocessing

    The collected texts are pre-processed to prepare them for analysis. This includes steps such as tokenization (splitting into words or phrases), removal of stop words, Lemmatization and punctuation correction.

  3. Feature extraction

    In this step, relevant features or characteristics are extracted from the pre-processed texts. These can be, for example, words, phrases, sentence lengths, emoticons and more.

  4. Training the model

    A sentiment analysis model is developed on the basis of training data. This training data consists of texts that have been manually assigned sentiment labels, e.g. "positive", "negative" or "neutral". 
    The model learns how different words and phrases are associated with certain sentiments by capturing statistical patterns in the training data.

  5. Classification

    Once the model has been trained, it can be used to classify sentiments in new texts. It analyzes the pre-processed texts and assigns sentiment categories to them, e.g. "positive", "negative" or "neutral".

  6. Evaluation and accuracy

    The results of the sentiment analysis are evaluated to check the accuracy of the model. This can be done using metrics such as accuracy, precision, recall and F1 score.

  7. Applications

    The sentiment analysis results can be used in various application areas, e.g. for monitoring brand reputation, identifying customer needs, recognizing trends and automatically classifying customer ratings.

If you want to use sentiment analysis without training it yourself, it is advisable to purchase a suitable IDP platform to be used. With Konfuzio is a versatile tool for the automatic processing of data. This also includes sentiment analysis. You can find more information here.

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For this you need the sentiment analysis

Companies can take advantage of sentiment analysis in the following areas:

Market research

Sentiment analysis enables companies to understand the opinions and preferences of their customers in real time. 

They analyze feedback on products and services in order to make product improvements and better meet customer needs.

Brand reputation and crisis management

Companies are constantly monitoring social media and other online platforms to determine how their brand is perceived. 

This enables them to react quickly to negative comments or crises and limit damage to their reputation.

Customer service

Sentiment analysis helps to prioritize customer inquiries and complaints and process them more quickly. 

Companies can also respond proactively to customer concerns before they escalate into serious problems.

Product development

By analyzing customer reviews and feedback, conclusions can be drawn about product improvements and new products or functions can be developed that meet customer needs.

Competitive analysis

Companies track the opinions and evaluations of their competitors in order to gain insights into their strengths and weaknesses and improve their own position in the market.

Marketing and advertising

Sentiment analysis helps companies to align their marketing and advertising measures by understanding how target groups react to campaigns. This enables a more targeted approach and improves marketing success.

Trend analysis

Companies can use sentiment analysis to identify trends in their industry or in society in general. This enables them to optimize the development of new business strategies and adapt to changing conditions.

Personnel management

Sentiment analysis is also used to assess the commitment and satisfaction of the workforce. 

If you want to identify problems in the working environment or improve the working atmosphere, sentiment analysis is a helpful approach. 

Overall, sentiment analysis enables companies to react more quickly to customer and market trends, be more competitive and base their business decisions on data-based insights. In this way, they achieve improved customer loyalty, higher sales and thus a competitive advantage.

Sentiment analysis models

There are different types of sentiment analysis models, which vary according to their complexity and intended use. 

The most common models are based on 2 different approaches:

  1. Lexicon-based models
  2. Models based on machine learning
  3. Hybrid models
  4. Unsupervised models
  5. Emotion detection models
  6. Aspect-based sentiment analysis

Lexicon-based models

These models use Sentiment dictionaries or Lists of words and their associated sentiments (e.g. positive, negative, neutral). 

You count the sentiment words in a text and determine the overall sentiment based on the frequency and weighting of the words.

Example:

  • AFINN (Affective Norms for English Words and Phrases): This is a well-known lexicon-based sentiment dictionary that rates English words and phrases with sentiment scores.
  • SentiWordNet: SentiWordNet links synsets (groups of words) in WordNet with sentiment scores to perform sentiment analysis.

Machine learning (ML) based models

  • Binary classification: These are simple ML models that divide texts into two classes, e.g. positive or negative. This is done using training data that contains texts with their associated sentiments.
  • Multi-class classification: In contrast to binary classification, these models divide texts into several sentiment classes, such as positive, neutral, negative or other gradations.
  • Recurrent neural networks (RNN): RNNs take into account sequences of words and their dependencies in the text, which makes them suitable for analyzing longer texts.
  • Convolutional Neural Networks (CNN): These models use convolutional operations to extract features from texts and assign sentiments. They are often effective in analyzing texts with recurring patterns.
  • Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU): These neural network architectures are well suited for processing text sequences and capturing dependencies over long distances.

Example:

  • Naive Bayes: A simple but effective ML model used for sentiment analysis by calculating the probability of a text belonging to a certain sentiment category.
  • Support Vector Machine (SVM): SVM is an ML algorithm used in sentiment analysis to classify texts into different sentiment categories.

Hybrid models

Hybrid models combine lexicon-based approaches with machine learning to leverage the strengths of both approaches and increase accuracy.

Example:

  • VADER (Valence Aware Dictionary and sEntiment Reasoner): VADER combines lexicon-based approaches with rules and machine learning to perform sentiment analysis. It is particularly useful for analyzing social media.
  • LSTM-CNN: A hybrid model that combines recurrent neural networks (LSTM) with convolutional neural networks (CNN) to analyze text and recognize sentiment.

Unsupervised models

These models do not require annotated training data and attempt to identify sentiments in texts without prior knowledge. They are usually based on clustering techniques or embeddings.

Example:

  • Latent Dirichlet Allocation (LDA): LDA is an unsupervised algorithm for topic modeling that can be used in aspect-based sentiment analysis.
  • K-Means clustering: K-Means describes a clustering algorithm that groups texts based on similarities, which can be used in unsupervised sentiment analysis.

Emotion detection models

These specialized models aim to recognize not only the sentiment, but also the emotions it contains. 

They are used in applications such as the Analysis of customer service chats and social media useful.

Example:

  • Affectiva: Affectiva is an application that specializes in emotion recognition and develops advanced models for recognizing emotions in facial expressions and speech.
  • EmoReact: EmoReact is a model that aims to recognize emotions in social media and comments in order to analyze the emotional feedback on certain topics or products.

Aspect-based sentiment analysis

Aspect-based sentiment analysis not only identifies the overall sentiment, but also the sentiments relating to specific aspects or features of a product or service. 

They are mainly used in Product evaluations and analyses helpful.

Example:

  • SemEval-2014 Task 4: This competition for aspect-based sentiment analysis has produced various models aimed at recognizing sentiment in relation to specific aspects of products or services.
  • Aspect-Based Sentiment Analysis with Gated Recurrent Neural Networks: This is a specialized neural network model that was developed for aspect-based sentiment analysis and recognizes sentiments in relation to certain aspects in texts.

The choice of the appropriate model depends on the specific requirements of the use case, including the type of texts processed, the available training data and the desired accuracy. 

In practice, you should evaluate and adapt different models to achieve the best results.

Advantages and disadvantages

There are various advantages and disadvantages that you should take into account when making your decision:

 

Advantages of sentiment analysisDisadvantages of sentiment analysis
1. automation: Fast and automatic analysis of large volumes of text.1. context problems: difficulties in recognizing irony, sarcasm and ambiguous terms.
2. real-time monitoring: the ability to respond to customer feedback and opinions in real time.2. accuracy: No model is perfect, and accuracy may vary depending on text type and quality.
3. scalability: Easily applicable to large data sets and text sources.3. data preparation: Requires time and resources to collect, cleanse and label training data.
4. trend recognition: Identifies trends and changes in public opinion.4. adaptability: models must be regularly updated and adapted to the changing context.
5. competitive advantage: Improves competitiveness and customer experience.5. emotional complexity: Sentiment analysis cannot always accurately capture the complexity of human emotions.

The advantages and disadvantages of sentiment analysis depend heavily on the quality of the data, the model architecture and the adaptation to the specific use case. If you are integrating sentiment analysis into your business processes, you should take these factors into account when making your decision.

Challenges

Sentiment analysis faces several challenges that can affect its accuracy and effectiveness. 

The five biggest are included:

  • Irony and sarcasm: Dealing with ironic or sarcastic statements is problematic because the text expresses the opposite of what is actually meant - without the model recognizing this.
  • Ambiguous terms: Words or phrases that have different meanings in different contexts can confuse sentiment analysis.
  • Negation and conjunctions: The use of negation words or conjunctions in a sentence can reverse or modify the overall sentiment, requiring more complex models for recognition.
  • Cultural differences and slang: Sentiment analysis models often recognize the wrong meaning of slang expressions and culturally different meanings of words or expressions.
  • Subtle sentiments:Subtle sentiments that are not obviously positive or negative are difficult to capture. These include, for example, mixed opinions or neutral evaluations with hidden emotions.

Overcoming these challenges requires progressive NLP techniques and a better model fit. It is important that you consider these aspects in order to achieve more accurate sentiment analysis results and minimize misunderstandings.

sentiment analysis use cases

Use cases for the application of sentiment analysis

Sentiment analysis is used in various industries. 

Five common examples are:

Social media monitoring

Companies can use sentiment analysis to track how their brand is perceived on social media in real time. 

They analyze customer feedback and opinions about their products and services in order to respond to trends and problems.

A technology company uses sentiment analysis to monitor the mood on social media in real time. They recognize negative comments and complaints about their products and react immediately to improve customer feedback.

Product ratings and reviews

Online retail companies use sentiment analysis to automatically analyze customer ratings and reviews. 

This helps to identify popular products, handle customer complaints and make product improvements.

An online retail company automatically analyzes customer reviews for its products. Thanks to sentiment analysis, they can determine that a new product is particularly popular and successfully add it to their range.

Customer service and support

You can use sentiment analysis in customer support chats or emails to assess the mood and satisfaction of your customers. 

On this basis, you will be able to respond more quickly to concerns and offer better customer service.

A telecommunications company uses sentiment analysis in its customer support chats. This allows them to assess customer satisfaction and identify when customers are dissatisfied in order to offer solutions quickly.

Market research

Sentiment analysis enables companies to recognize market trends and customer preferences. This helps to identify new business opportunities, evaluate competitors and adapt marketing strategies.

A food manufacturer uses sentiment analysis to analyze consumer opinions about new product flavors. They recognize which flavours are best received and adapt their product line accordingly.

Brand reputation and crisis management

If you want to monitor your online reputation and react quickly to negative feedback or crises, sentiment analysis is an important asset. 

You can use it to analyse public opinions and reviews to identify potential problems and minimize the impact on your brand. This allows you to take countermeasures in good time and limit the damage to your company's reputation.

A hotel chain uses sentiment analysis to monitor online reputation. If negative reviews emerge, they can respond immediately to minimize the damage to their brand and restore customer trust.

These examples show how you can use sentiment analysis in different contexts to gain valuable insights into customer and market attitudes and react accordingly.

Conclusion - A transformative technology with promising future prospects

As a technology, sentiment analysis has a transformative effect on various areas, including marketing, customer service and market research. 

The ability to analyze customer feedback in real time and respond to trends is changing the possibilities for companies in the long term.

The future of sentiment analysis promises further developments:

  • With advances in the areas of machine learning and NLP sentiment analysis models are becoming increasingly precise and adaptable. 
  • The integration of AI and automated processing further increases efficiency and accuracy.

Despite the challenges, sentiment analysis is a driving force for better decision making, improved customer service and the identification of trends. 

With the right approach and an understanding of its strengths and weaknesses, you can use sentiment analysis to provide you with a wealth of valuable insights and opportunities that will in turn give you a competitive advantage.

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