The Complete Guide to Sentiment Analysis in Business

Sentiment analysis is an artificial intelligence solution that identifies emotions in text and voice recordings. This technology has become an essential tool for companies wishing to understand their customers, offering valuable insights into customer satisfaction, brand perception and market trends.

This article gives you an overview of the subject, from its definition to its concrete applications, including its technical operation and limitations. You'll discover how this technology can transform your customer relationships and boost your sales success.

What is sentiment analysis?

Definition

Sentiment analysis is an artificial intelligence technique that identifies the emotional tone of a text or voice recording. It uses natural language processing and machine learning to classify emotions expressed in unstructured data.

This method can detect whether a text or voice recording expresses a positive, negative or neutral feeling. It can also detect more specific emotions such as joy, sadness, anger or disgust.

Sentiment analysis examines words, phrases and context to understand the opinion being expressed. In the case of voice data, it also relies on acoustic analysis to assess the speaker's tone and emotions. This approach is particularly useful for analyzing customer interactions in call centers.

By combining text and voice analysis, sentiment analysis offers valuable insights into customer opinions and emotions, enabling companies to improve user experience and satisfaction.

The different types of sentiment analysis

There are several types of sentiment analysis, each meeting specific needs:

• Binary analysis: classifies texts as positive or negative. For example, to quickly assess customer satisfaction based on online comments.
• Fine (or graded) analysis: adds intermediate categories such as "very positive", "neutral" or "very negative". Useful for a more nuanced assessment of feelings.
• Emotion analysis: detects specific emotions such as joy, fear or surprise. Used in particular to analyze reactions to an advertising campaign or event.
• Aspect-based analysis: evaluates feelings for different aspects of a product or service (e.g. quality, price, design). Identifies customers' perceived strengths and weaknesses.
• Intent-based analysis: identifies the intent behind a text (query, complaint, purchase, etc.). Helps automate the processing of customer requests and redirect them to the right department.

Each type of sentiment analysis has its own strengths and can be used according to your specific needs. A hybrid approach combining different methods can also be considered for a more complete and accurate sentiment analysis.

What's the point of sentiment analysis or opinion mining?

Sentiment analysis, also known as opinion mining, is a powerful tool for optimizing your strategy and making data-driven decisions thanks to its many advantages:

1. Customer insight: You can better understand what your customers think of your products or services, by analyzing their comments and feedback. This enables you to identify their needs and expectations.
2. Product improvement: Negative feedback can help you identify areas for improvement in your products or services. By taking constructive criticism into account, you can optimize your offering and boost customer satisfaction.
3. Crisis management: You can detect emerging issues early and respond to them before they escalate. Sentiment analysis alerts you to spikes in negative sentiment, enabling you to proactively manage crises.
4. Competitor analysis: You can compare sentiment towards your brand with that of your competitors. This gives you a competitive edge and enables you to adjust your strategy accordingly.
5. Trend forecasting: Changes in sentiment can indicate future trends. By analyzing the evolution of opinions in your market, you can anticipate future opportunities and challenges.
6. Time-saving automation: Sentiment analysis automates the processing of customer feedback, saving you precious time compared with manual analysis. So you can concentrate on exploiting the insights you gain.

Advantages and disadvantages of emotion analysis

Advantages:

• Rapid analysis of large amounts of data
• Objectivity in assessing feelings
• Detection of subtle emotional nuances
• Data-driven decision support

Disadvantages:

• Difficulty understanding sarcasm or irony
• Need quality data to be accurate
• May lack cultural or personal context
• Risk of bias in learning models

Emotional analysis is a powerful tool, but it must be used with care. It is recommended to combine it with other methods (surveys, interviews, etc.) to obtain a more complete and nuanced understanding of customer feelings.

Pierre et Vacances Center Parcs Group case study

Groupe Pierre et Vacances Center Parcs (PVCP) used Batvoice AI's Speech analytics solution, integrating sentiment analysis, to better understand its customers' needs and frustrations. This approach identified a major irritant: incomprehensible invoices for customers, which represented 12% of their reasons for calling.

Analysis of the emotions expressed by customers during calls revealed a high proportion of negative feelings, such as frustration and anger, associated with this topic. These insights prompted PVCP Group to simplify and clarify the format of its invoices, reducing friction in the customer journey and improving overall satisfaction.

This example illustrates how sentiment analysis applied to customer interactions can highlight specific issues and guide a company's strategic decisions. By exploring the emotions expressed by customers, sentiment analysis offers a finer understanding of the customer experience, enabling targeted and effective improvements.

​

Use cases for sentiment analysis in the workplace

Improving customer service

Sentiment analysis helps identify positive and negative customer experiences. You can detect problems early and respond to them before they escalate. This enables you to better target your loyalty and retention efforts.

You can also use this data to train your customer support team. Teach them how to deal effectively with negative feedback and interactions, and how to reinforce the positive aspects of the customer experience.

Track the evolution of sentiment over time. You'll be able to measure the impact of your efforts on your customer service and overall performance. A McKinsey study shows that companies that use sentiment analysis to track and improve their customer experience can reduce costs by 20-30%, increase customer satisfaction by 10% or more, and boost sales.

Track the performance of marketing campaigns

Sentiment analysis is used to evaluate the effectiveness of marketing campaigns by measuring audience reactions to messages. Sentiment is compared before and after the launch of a campaign, to determine whether it has had the desired effect on the audience.

As well as measuring the overall impact of a campaign, sentiment analysis can also identify the specific elements that generate the most positive reactions. This information is invaluable for replicating these successes in future initiatives. In addition, this analysis can also identify potential influencers and brand ambassadors, whose support can amplify the message's reach.

However, not all reactions are positive. That's why early detection of negative comments is crucial, especially on social networks where they can quickly go viral. By identifying these negative reactions, companies can react swiftly to limit potential damage.

Conducting market research

Sentiment analysis is a powerful tool for market research, enabling us to understand the opinions and preferences of target customers. It offers several key applications:

1. The study of competitors' product reviews, revealing opportunities to improve your own offerings. This approach enables you to stand out from the competition by offering solutions that are better adapted to customer needs.
2. The analysis of trends in a specific sector, which enables a company to anticipate market developments and adapt its strategy. For example, if the analysis reveals a growing interest in eco-friendly products, a company may decide to invest in the development of more sustainable offerings.
3. Testing new product ideas before they are launched, analyzing public reactions to refine concepts. This approach reduces the risk of failure by ensuring that new products are in tune with market needs.

Monitor brand reputation online

Sentiment analysis is an essential tool for monitoring and managing a brand's online reputation. By monitoring mentions of the company on social networks, forums and review sites, it provides an overview of the public's perception of the brand.

This monitoring makes it possible to react quickly to negative comments, limiting their potential impact on brand image. An appropriate response can even turn a delicate situation into an opportunity to demonstrate the company's commitment to customer satisfaction.

What's more, sentiment analysis helps identify the brand's biggest online advocates. By engaging with these natural ambassadors, companies can build support and encourage positive word-of-mouth.

Finally, sentiment analysis compares a brand's reputation with that of its competitors. This comparison highlights the company's relative strengths and weaknesses in the market, providing valuable information for adjusting its strategy.

How does sentiment analysis AI work?

The different analysis methods

There are several approaches to sentiment analysis, each with its own characteristics and advantages:

1. The rule-based method uses predefined lists of positive and negative words to determine the polarity of a text. Easy to implement, it can however be limited in its ability to capture nuance and context.
2. The machine learning approach involves training models on large quantities of annotated data. This method enables more precise and adaptive analysis, but requires a large volume of high-quality training data.
3. The hybrid method combines predefined rules with machine learning, leveraging the strengths of each technique.
4. Acoustic analysis is a specific method for analyzing feelings in speech data using Speech Analytics. It focuses on the non-verbal characteristics of speech, such as intonation, rhythm, volume and pauses, to determine the emotions and feelings of the speaker. This method complements text-based approaches to speech transcription.

The choice of method will depend on the specific needs of the project, the resources available and the desired level of accuracy. It is often beneficial to combine these approaches with different types of sentiment analysis to obtain the best results.

Collection of various text and voice data

Data collection is the first step in sentiment analysis. It involves gathering text and voice recordings from a variety of sources, including :

• Recorded customer calls
• Customer comments and reviews on the company's website, app or third-party platforms
• Publications, messages and interactions on social networks (Facebook, Twitter, Instagram, etc.)
• E-mails and chat conversations with customer service

The aim is toobtain a varied and representative sample of the target audience, from different customer segments, time periods and interaction contexts. Data diversity is essential to guarantee the accuracy and generalizability of the analysis.

It's also important to remember that data collection must comply with privacy and personal data protection regulations, such as the RGPD.

Natural language processing (NLP)

Natural language processing (NLP) plays an essential role in sentiment analysis. It enables artificial intelligence to understand and interpret human language. NLP is based on several key steps:

1. Tokenization: this stage involves breaking down the text into smaller units, called tokens, which can be words, phrases or other significant elements. This process helps structure the text for subsequent stages.
2. Stop words: stop words are frequent words that don't add any significant meaning to the sentence, such as "le", "la", "de", "en", etc. Removing them reduces the noise in the data and allows us to focus on words that do carry meaning. Removing them reduces the noise in the data, allowing us to concentrate on the words that do make sense.
3. Lemmatization: the process of reducing words to their basic form or lemma. For example, "mangeait", "mangeons" and "mangeront" will be reduced to their basic form "manger". Lemmatization makes it possible to group different forms of the same word together for a more pertinent analysis.
4. Syntactic analysis: the aim of this stage is to identify the grammatical structure of the sentence by determining the relationships between words. It enables us to understand the role of each word in the sentence (subject, verb, complement, etc.) and to extract meaning.

These NLP techniques prepare text for sentiment analysis. They help AI to extract the meaning and emotions conveyed by language by structuring textual data in a way that the machine can understand.

Classification and polarization of feelings

Once the NLP has pre-processed the text data, the next step is to classify the sentiments expressed. The AI assigns a polarity to each text: positive, negative or neutral. More advanced systems can detect specific emotions, such as joy, anger, sadness, surprise or fear.

To classify feelings, AI compares pre-processed text with models previously trained on annotated data. It also often calculates a sentiment intensity score, indicating the strength or confidence of the prediction.

Sentiment classification and polarization enable the opinions and emotions expressed in large volumes of textual data to be measured quickly and automatically.

Context analysis

Context is essential for accurate sentiment analysis. AI needs to understand the nuances of language, such as sarcasm, irony and idiomatic expressions, which can be misinterpreted without taking context into account. Advanced models use contextual analysis to capture these nuances by examining surrounding sentences and the overall structure of the text.

For example, the sentence "That movie was so good I almost fell asleep" could be interpreted as positive without contextual analysis. However, an advanced model would understand sarcasm and correctly classify this sentence as expressing a negative sentiment.

Contextual analysis greatly improves the accuracy of sentiment analysis, but requires large amounts of annotated training data and significant computational resources. Despite these challenges, recent advances in this field have led to significant progress in understanding the nuances of language.

Acoustic analysis of voice data

For voice data, we use Speech Analytics, a specialized AI that combines analysis of textual content with analysis of acoustic aspects. This multimodal approach provides a more complete and nuanced understanding of the sentiments expressed.

Speech analytics examines acoustic elements such as :

• Tone and intonation of voice
• Speech rhythm and flow variations
• Pauses and silences
• Vocal intensity and volume variations
• Spectral characteristics, such as mel frequency cepstral coefficients (MFCC)

These acoustic cues provide valuable information about the speaker's emotions and state of mind. For example, a strained voice and rapid delivery can suggest anger or stress.

Speech analytics uses audio feature extraction techniques and emotion classification models to analyze these acoustic aspects. By combining this information with the analysis of textual content obtained from automatic speech transcription, AI can more accurately interpret the feelings conveyed in speech data.

Viewing results

The final step in the sentiment analysis process is to present the results clearly, concisely and visually. The main objective is to make the insights accessible and exploitable by the company's various stakeholders.

To do this, the AI generates various informative visualizations, such as :

• Pie charts or bar graphs showing the distribution of feelings (positive, negative, neutral) within the data analyzed
• Word clouds highlighting the terms most frequently associated with each feeling
• Interactive dashboards to explore results along different dimensions (e.g. by product, by geographic region, by period)
• Curve graphs illustrating changes in sentiment over time, allowing you to identify trends or significant changes.

These visualizations help users to quickly understand key trends and identify areas requiring particular attention. They also make it easier to communicate sentiment analysis results to various stakeholders, whether marketing, customer service or company management.

Based on these clear, informative visualizations, decision-makers can take informed action to improve their products, services and overall customer experience, taking into account the feelings and opinions expressed by their customers.

Challenges and limitations of sentiment analysis AI tools

AI sentiment analysis presents several significant challenges. Firstly, understanding context and linguistic nuance remains difficult for machines. Tools struggle to detect sarcasm, humor, abbreviations and new internet terms, which can skew results.

The quality of training data is another major issue. Biases in the data, for example due to over-representation of certain demographic or opinion groups, can lead to erroneous or discriminatory analyses.

Finally, privacy is a crucial issue. Companies must be transparent about their collection and use of personal data, and offer customers the possibility of controlling how their information is used.

By keeping these challenges and limitations in mind, companies can make the most of AI-based sentiment analysis tools, while ensuring responsible and ethical use of these technologies.

​