Sentiment analysis is a term that’s often misunderstood despite its widespread use.
This guide aims to clarify sentiment analysis, from its applications to the underlying mechanics and NLP techniques. Also check the source of the best software development companies.
What is Sentiment Analysis?
Sentiment analysis, or opinion mining, involves identifying the emotional tone (positive, negative, or neutral) expressed by someone regarding a topic or event. It’s widely used in social listening and online reputation management to understand consumer attitudes towards a brand, company, product, or individual.
Applications of Sentiment Analysis
Sentiment analysis is a crucial feature in social listening tools. Here are key ways it can be utilized:
1. Monitor Brand Health
Use sentiment analysis to gauge the health of your brand by tracking the ratio of positive to negative mentions. Analyze key themes within this feedback to understand common praises and complaints. A useful technique is to calculate net sentiment, a specific formula to measure this balance.
2. Identify Reputation Crises Early
Regularly check your social listening dashboard for spikes in negative mentions to spot potential crises early. Tools like Awario provide insights into these spikes, helping you understand their causes and manage them before they escalate. This analysis helps preserve brand reputation by identifying the roots of performance issues.
3. Track Campaign Performance
Similar to brand monitoring, sentiment analysis can track reactions to marketing campaigns, collaborations, and events. Use it to measure overall sentiment and pinpoint reasons behind significant shifts. Awario’s multilingual sentiment analysis enables global campaign tracking.
4. Conduct Competitor Analysis
By monitoring competitors’ sentiment, you can learn which aspects of their products are well-received or criticized. This information can serve as a benchmark for evaluating your own brand’s performance. For example, if 50% of your mentions are positive, 40% are negative, and 10% are neutral, competitor data can help you determine if this is favorable.
5. Enhance Customer Service
Understanding customer sentiment towards your products or services can help prioritize customer support efforts. Addressing negative mentions promptly, especially those with high reach, can turn situations around and improve the overall customer experience. Tools like contact center software and sentiment analysis platforms are valuable for this purpose.
Investing in sentiment analysis tools and monitoring systems is essential for informed decision-making and effective management of brand reputation.
How Sentiment Analysis Works?
Sentiment analysis is a classification task where an algorithm determines if a piece of text is positive, negative, or neutral. This is achieved using Natural Language Processing (NLP) through three primary methods: supervised machine learning, rule-based techniques, or a combination of both.
1. Supervised Machine Learning (ML)
In supervised ML, the system is trained on a labeled dataset where human evaluators have already categorized the sentiment of documents. The computer learns from this data and applies the learned classifiers to new text inputs. Various algorithms and neural networks, such as Naive Bayes, logistic regression, and Support Vector Machines, can be used. The system assigns scores to words, phrases, and topics, reflecting their sentiment on a scale from -1 (extremely negative) to 1 (extremely positive). The overall sentiment score is the average of these individual scores. The researcher defines the sentiment boundaries, for example, -1 to -0.33 for negative, -0.33 to 0.33 for neutral, and 0.33 to 1 for positive.
- Pros: These techniques allow for customized models tailored to specific data analysis needs.
- Cons: Models may not adapt well across different domains or writing styles.
2. Rule-Based Methods
Rule-based systems use a set of predefined rules and sentiment lexicons (dictionaries of labeled words) to analyze text. The system identifies words from the lexicon in the text and calculates the average sentiment score, similar to the ML method. For example, in the sentence “The coffee was okay, but the food was terrible,” the sentiment score would be -0.5 if “okay” has a score of 0 and “terrible” a score of -1.
- Pros: Easier to build and less resource-intensive than ML, with full control over the vocabulary.
- Cons: Limited to the words in the lexicon and can become complex with additional rules. Continuous fine-tuning is necessary for dynamic environments.
3. Hybrid Approaches
Hybrid methods combine supervised ML and lexicon-based techniques to enhance sentiment accuracy and performance. Typically, a rule-based system first attempts to classify sentiment. If the confidence level is low, a machine learning classifier steps in to analyze the sentiment. This approach leverages the speed of rule-based systems and the accuracy of ML models.
- Pros: Benefits from both methods, offering better insights and performance.
- Cons: More time and effort required to develop these systems.
4. Multilingual Support
The sentiment algorithm supports all languages. Extensive testing has shown high accuracy for popular languages like English, French, Spanish, German, and Portuguese, and over 65% accuracy for other languages.
How Accurate is Sentiment Analysis?
The accuracy of sentiment analysis refers to how closely the system’s output matches human evaluations. However, it’s important to note that even human raters only agree between 65% and 80% of the time. This suggests that human sentiment analysis accuracy ranges from 65% to 80%.
Since sentiment is often subjective, measuring accuracy can be challenging. Generally, a sentiment analysis system needs to be at least 50% accurate to be considered useful, with over 65% accuracy deemed good. For instance, Awario’s sentiment analysis accuracy is just over 70%, which is comparable to human performance.
Accuracy isn’t always the best metric to judge an algorithm’s quality. For example, a model that labels every passenger as not a terrorist might achieve over 99% accuracy, given the extremely low incidence of terrorists among millions of passengers. However, such a model wouldn’t be practical for real-world applications like airport security.
Key Metrics: Precision and Recall
In addition to accuracy, precision and recall are crucial for evaluating algorithms. Precision is the percentage of correctly identified instances among all instances labeled as a particular category by the system. Recall is the ratio of correctly identified instances to all actual instances in the dataset.
For example, in a dataset of 10 statements (7 positive, 3 negative), if a system identifies 5 statements as positive and only 3 are correct, the precision is 3/5 and the recall is 3/7.
Challenges in Sentiment Analysis
- Sarcasm: Detecting sarcasm is difficult for machines due to the lack of context in short texts, like social media posts. For example:
- “It only took me 5 minutes to get a coffee at Starbucks. Great start of the day!”;
- “It only took me 30 minutes to get a coffee at Starbucks. Great start of the day!”
- Negations: Identifying and interpreting negations can be challenging for computers. For instance:
- “I wouldn’t say the coffee was particularly good.”;
- “I was hoping the coffee would be great. It wasn’t.”
- Ambiguity: Some words can have different sentiments based on context. For example:
- “I tend to drink ice-cold coffee in the summer.”;
- “When I finally got my coffee, it was ice cold.”
- Multipolarity: Texts often express multiple emotions towards different subjects, requiring aspect-based sentiment analysis. For instance:
- “Starbucks coffee is much better than Dunkin’.”
Conclusion
Sentiment analysis, while complex, can be integrated into daily marketing routines with the right tools and approaches. Despite its challenges, it remains a valuable asset for understanding consumer sentiment and enhancing marketing strategies.