Companies struggle to derive value from their data to understand customer sentiments. What are customers talking about? How do their products and services perform in the market? Answering these questions is crucial. Sentiment analysis techniques can uncover valuable insights from customer feedback spread across social media, forums, review websites, and more.
What is Sentiment Analysis?
Sentiment analysis uses Natural Language Processing (NLP) to determine whether opinions extracted are positive, negative, or neutral. Companies perform sentiment analysis on texts such as customer feedback on brands/products to better understand their opinions.
Types of Sentiment Analysis
Sentiment analysis helps determine the polarity of opinions as positive, negative, or neutral. It is also useful for extracting and analyzing emotions like anger, happiness, sadness, etc.
Sentiment analysis is also used to understand the importance or urgency of a task and to assess the interest of a group or individual in something. Data scientists and data modeling experts can design sentiment analysis according to their needs.
Here are some popular types of sentiment analysis:
Fine-Grained Sentiment Analysis
This type of analysis helps understand the degree of polarization of emotions. For example, a pre-election opinion poll shows possible voting orientations of people. This analysis provides a deeper understanding by expanding polarity categories such as:
- Strongly Agree
- Agree
- Neutral
- Disagree
- Strongly Disagree
This analysis is similar to a 5-point scale where “Strongly Agree” is 5 stars and “Strongly Disagree” is 1 star.
Emotion Detection
Sentiment analysis is designed to extract and understand emotions. In this case, lexical analysis is particularly important; however, the strong use of dictionaries may present a disadvantage, as the same set of words can have opposite meanings depending on culture and context.
Therefore, sentiment analysis with Machine Learning algorithms tends to be more useful in this particular case, and emoticons are increasingly used to avoid misunderstandings.
Intent Analysis
Sentiment analysis can be designed to understand if a customer or potential customer is interested in a particular product and can also help companies determine if a customer intends to make a purchase.
Aspect-Based Sentiment Analysis
This technique helps companies conduct in-depth research on qualitative customer feedback. For example, when customers talk about the shelf life of a product, the user experience at a retail location, or the response time to an online or phone query, their tone can be positive, negative, or neutral.
Complex Machine Learning algorithms can help determine the underlying sentiment in such statements. For instance, if a customer says, “The response time for online inquiries is too long,” the algorithm can identify a negative sentiment.
Multilingual Sentiment Analysis
Multilingual sentiment analysis is more challenging and complex because it involves more than one language. Text identification algorithms can facilitate the process by detecting the language in the text, and pre-trained models can convert the text into the chosen language.
How to Perform Sentiment Analysis and the Role of Artificial Intelligence in Sentiment Analysis
There are several methods to evaluate emotions in texts. Let’s simplify them:
Rule-Based Systems: Sentiment analysis can use predefined rules to determine sentiment based on specific words or phrases. Positive words indicate a positive sentiment, and negative words indicate the opposite. Although simple, it might lack nuance.
Machine Learning Algorithms: Machine learning models, such as Support Vector Machines or Naive Bayes, learn from labeled data to predict sentiment. They can handle complexity and nuances, making them widely used in sentiment analysis tasks.
Deep Learning Models: Deep learning techniques, such as Recurrent Neural Networks (RNN) or Long Short-Term Memory (LSTM) networks, process sequential data and can capture complex patterns, improving sentiment analysis accuracy.
Hybrid Models: Combining rule-based systems, machine learning algorithms, and deep learning models can create hybrid approaches. These models leverage the strengths of each method, offering a more comprehensive analysis.
ChatGPT for Sentiment Analysis: ChatGPT, based on advanced Natural Language Processing (NLP), is a revolutionary way to analyze sentiment. By conversing with the model and examining its responses, you can assess underlying sentiment. Its contextual understanding and extensive knowledge make it adept at interpreting emotions in textual conversations. Additionally, it is user-friendly and does not require deep technical knowledge.
The choice of method depends on the specific use case and the complexity of the sentiment analysis task at hand.
Blue BI has successfully applied sentiment analysis in various use cases:
Pharmaceutical:
- Analyzing emails received from Drug Vigilance to identify which drugs have caused the most issues.
- Analyzing emails from doctors for technical/informatic issues and providing automated support.
Fashion:
- Detecting defects/imperfections in clothing perceived by customers, identifying potential production issues.
- Real-time analysis of social media comments during fashion shows to understand user opinions.
Forums/Ticketing Systems:
- Analyzing support tickets to understand frequent issues and manage them correctly; providing retrospective feedback to identify root causes and implement corrective solutions.
- Providing automatic solutions to issues and detecting feedback on their effectiveness.
Hospitality:
- Understanding and measuring customer satisfaction regarding a stay, experience, or service associated with a hospitality establishment.
Advantages of Sentiment Analysis
- Automation of Decisions: Sentiment analysis can automate the process of evaluating user or customer opinions, enabling companies to make data-driven decisions more efficiently.
- Understanding Customer Feedback: It allows companies to better understand how their products or services are perceived, identifying strengths and weaknesses.
- Timely Response: It enables companies to respond promptly to customer comments or reviews, enhancing customer satisfaction and brand reputation.
- Marketing Strategy Planning: Sentiment analysis can help identify market trends and guide marketing strategy planning based on customer preferences.
- Competitive Monitoring: It helps companies distinguish themselves in the market and from competitors.
- Reputation Crisis Detection: It enables quick identification of crisis situations and taking corrective measures to preserve brand reputation.
Disadvantages of Sentiment Analysis
- Complexity of Texts: Sentiment analysis can be complex due to the variety of expressions, puns, and informal language used in online communications.
- Ambiguity and Sarcasm: Statements can be ambiguous and contain sarcasm or irony, making accurate interpretation difficult.
- Language Limits: Some sentiment analysis algorithms may be more effective in certain languages or cultures than others.
- Training Requirements: Many models require initial training on labeled data, which can be time-consuming and resource-intensive.
- Limited Generalization: Some models may struggle to generalize well to new contexts or capture complex nuances of sentiment.
Blue BI’s Presentation at the BD&BA Observatory Conference
Blue BI presented its perspective on Sentiment Analysis at the conference “Data Culture & Generative AI: Towards a New Data Experience” of the Big Data & Business Analytics Observatory. If you want to learn more, click here.
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