Sentiment analysis is the process of analysing digital text to determine if the emotional tone of the message is positive, negative, or neutral. Today, companies have large volumes of text data like emails, customer support chat transcripts, social media comments, and reviews. Sentiment analysis tools can scan this text to automatically determine the author’s attitude towards a topic. Companies can use the insights from sentiment analysis to evaluate staff attitudes to their work, their workplace or their product or service and its customers.
Why is sentiment analysis important?
Sentiment analysis, also known as opinion mining, is an advanced business intelligence tool that employs artificial intelligence (AI) to interpret and analyse human emotions from textual data. By integrating sentiment analysis into HR practices, our medium-sized company can gain valuable insights into employee opinions, improve HR services, and enhance overall employee satisfaction and engagement.
Traditional HR methods, such as manual reviews and surveys, are susceptible to personal biases and inconsistencies. Sentiment analysis leverages AI-based tools to provide objective and consistent results when analysing employee feedback. This ensures that HR decisions are based on accurate and unbiased data, promoting fairness and equity within the organisation.
Example: Consider an employee survey response like:
“I’m thrilled with the flexibility of my work hours but concerned about the lack of professional development opportunities.”
Traditional analysis might overlook the negative sentiment regarding professional development. However, sentiment analysis tools can objectively categorise and highlight both positive and negative sentiments, ensuring comprehensive insights.
Sentiment analysis enables HR to refine and enhance its programs and services based on genuine and specific employee feedback. By identifying the aspects of HR initiatives that elicit positive or negative sentiments, the company can prioritise improvements that align with employee needs and preferences. For example, if sentiment analysis reveals widespread dissatisfaction with the current onboarding process, HR can collaborate with relevant departments to streamline and improve this experience.
As a company grows, the volume of unstructured data from sources like emails, chat transcripts, surveys, and employee feedback increases exponentially. Cloud-based sentiment analysis tools allow HR to efficiently scale the analysis process, uncovering employee emotions across diverse and extensive datasets without incurring prohibitive costs. In a rapidly changing workplace, timely responses to employee sentiments are essential. Sentiment analysis software provides real-time insights into how employees feel about various aspects of their work environment. HR can configure these tools to send alerts for specific negative sentiments, enabling immediate action to address potential issues before they escalate into significant problems.
Sentiment analysis offers a number of advantages:
- It can provide objective insights through consistent analyses that eliminate personal biases associated with human reviewers, ensuring that employee sentiments are evaluated consistently.
- It can differentiate between positive, negative, and neutral sentiments, providing a clear picture of employee feelings.
- It can process large volumes of unstructured data from multiple sources, providing comprehensive insights without overwhelming HR resources.
- It is cost-effective using cloud-based tools to manage and analyze data affordably, allowing HR to focus on strategic initiatives.
- It detects negative sentiments promptly, enabling HR to address issues immediately and prevent escalation.
- It allows HR to adapt strategies and programs in real-time based on current employee sentiments and trends.
How does it work?
Sentiment analysis uses natural language processing (NLP) technologies to train computer software to interpret text in a manner similar to humans. The process typically involves several stages:
- Tokenisation: Breaks down sentences into individual elements or tokens.
- Lemmatisation: Converts words to their root forms (e.g., “am” becomes “be”).
- Stop-word Removal: Filters out non-essential words that do not contribute meaningful value (e.g., “with,” “for,” “at,” “of”).
- Sentiment Scoring: Assigns sentiment scores to extracted keywords, indicating the emotional tone of the text. For instance, a score of 10 represents satisfaction, while 0 indicates disappointment.
Sentiment Analysis may be rule-based where predefined lexicons are used to classify sentiment based on specific keywords. While straightforward, this approach can be challenging to scale and may lack cultural sensitivity. Alternatively, machine learning algorithms are used to train models on large datasets, enabling more accurate and context-aware sentiment detection. However, ML models are typically specific to the domain that they were trained on. It is also possible to use a hybrid approach that combines rule-based and ML approaches to leverage the strengths of both methods, with the goal of enhancing speed and accuracy in sentiment classification.
Sentiment analysis may be implemented with different outcomes in mind.
- Fine-grained scoring categorises text into multiple levels of emotion on a scale (e.g., 0-100), providing detailed sentiment insights.
- Aspect-based analysis focuses on specific aspects or features of a product or service, identifying sentiments related to each aspect.
- Intent-based analysis seeks to understand the underlying intent behind a conversation, such as identifying potential churn risks or interest in specific initiatives.
- Emotional detection analyses the psychological state of individuals by identifying emotions like joy, anger, sadness, and regret in their communication.
How can sentiment analysis be used?
Customer support teams use sentiment analysis to personalise responses based on the mood of the conversation. Similarly, HR can use sentiment analysis tools to tailor its interactions with employees, ensuring that responses are empathetic and appropriate to the employee’s emotional state.
Organisations constantly monitor mentions and conversations about their brands on social media, forums, blogs, and other digital platforms. Sentiment analysis allows HR and the public relations team to stay informed about how the company is perceived as an employer. This proactive monitoring helps address negative perceptions and leverage positive sentiments to attract and retain top talent.
Sentiment analysis systems help HR understand employee satisfaction and engagement by analysing feedback from surveys, reviews, and social media interactions. By identifying key drivers of positive and negative sentiments, HR can implement targeted initiatives to enhance the employee experience.
HR teams frequently run campaigns aimed at improving workplace culture, enhancing benefits, or promoting professional development. Sentiment analysis tools enable HR to track the effectiveness of these campaigns by measuring the emotional response from employees. If the overall sentiment is not as expected, HR can adjust the campaign strategies accordingly.
What are the challenges in sentiment analysis?
Despite advancements in natural language processing (NLP) technologies, understanding human language is challenging for machines. They may misinterpret finer nuances of human communication, such as those given below.
Sarcasm is extremely difficult for a computer to analyse sentiment in sentences that comprise sarcasm. Consider the following sentence, Yeah, great. It took three weeks for my order to arrive. Unless the computer analyses the sentence with a complete understanding of the scenario, it will label the experience as positive based on the word great.
Negation is the use of negative words to convey a reversal of meaning in the sentence. For example, I wouldn’t say the subscription was expensive. Sentiment analysis algorithms might have difficulty interpreting such sentences correctly, particularly if the negation happens across two sentences, such as, I thought the subscription was cheap. It wasn’t.
Multipolarity occurs when a sentence contains more than one sentiment. For example, a product review reads, I’m happy with the sturdy build but not impressed with the colour. It becomes difficult for the software to interpret the underlying sentiment. Aspect-based sentiment analysis is needed to extract each entity and its corresponding emotion.