What is sentiment analysis?
Sentiment analysis is a practice of machine learning that trains computers to discern the emotions expressed in written text.
In its basic form, a sentiment analysis model uses artificial intelligence to decide whether phrases express a positive, negative, or neutral sentiment.
However, many human statements are complex. Developing effective sentiment analysis involves addressing challenges such as double negatives, unusual syntax, sarcasm, names, and words with conflicting meanings. Understanding a sentence or phrase often depends on complex contexts that are challenging to elucidate.
Sentiment analysis, tracing its roots to the early days of computer science in the 1960s, has undergone significant maturation, particularly in the Web 2.0 era. This evolution is marked by the accessibility of extensive language datasets sourced from reviews, social media, and linguistic corpora, contributing to the refinement and expansion of sentiment analysis methodologies.
In the past two decades, data scientists have found many ways to enhance the efficacy of sentiment analysis tools. Today, the prominence of generative AI makes sentiment analysis a hot topic.
Other terms for sentiment analysis include opinion mining and emotion AI. It differs from semantic analysis, which centers on extracting meaning and understanding relationships between words in text, encompassing context, entities, and syntactic structures.
Business use cases for sentiment analysis
There are applications of sentiment analysis in any sector that generates significant amounts of written data – from customers, clients, users, or employees. But for the technology-driven organization of 2024, some specific use cases of sentiment analysis can stimulate business growth.
Brand reputation management
In the era of social media and online forums, organizations keenly track the public sentiment surrounding their brand. Leveraging sentiment analysis, companies sift through social media comments, forum discussions, and online mentions. This real-time, unfiltered feedback allows for a nuanced understanding of how the brand is perceived. Businesses can proactively address issues and maintain a positive brand image by swiftly identifying emerging trends and sentiments. Sentiment analysis thus empowers quick responses to negative sentiment, contributing to effective brand reputation management.
Customer service enhancement
Customer support teams navigate the intricacies of customer interactions using sentiment analysis. Businesses gain insights into customer satisfaction levels by analyzing customer support chat transcripts, emails, and service interactions. The immediate identification of urgent matters and personalized responses tailored to the emotional tone of the customer contribute to enhanced customer service.
Sentiment analysis enables real-time adaptation of responses, ensuring that customer interactions are efficient and personalized, ultimately improving the overall customer experience. In the future, sentiment analysis will be an important pathway to customer success.
Market research
In market research, sentiment analysis stands out as a robust analytical instrument. Employed by businesses to extract insights from diverse sources such as online review sites, survey responses, and social media comments, this analytical methodology utilizes a data-rich reservoir. This dataset, characterized by its depth and breadth, furnishes invaluable feedback about distinct product features. Companies leverage this feedback to drive innovation aligned with customer preferences and promptly attend to issues brought forth by users.
Campaign performance tracking
Marketers orchestrate campaigns precisely, and sentiment analysis is pivotal in tracking their performance. Businesses gain real-time insights into how the audience responds by analyzing social media conversations, comments, and feedback during the campaign period. This dynamic tracking allows for immediate adjustments based on sentiment analysis to maximize campaign effectiveness. Sentiment analysis gives marketers a real-time gauge of public sentiment about the campaign, enabling swift adjustments to optimize overall performance.
Product development
In product development, tech companies harness sentiment analysis on user reviews to guide their innovation. Analyzing sentiments expressed in user reviews sheds light on specific features or aspects that users find appealing or problematic. This direct feedback from end-users becomes a valuable guide for product development teams, helping prioritize features based on customer sentiment and enhancing overall user satisfaction. Sentiment analysis thus provides a detailed understanding of user emotions, steering product development towards meeting customer expectations.
Employee feedback analysis
Companies prioritize employee well-being by utilizing sentiment analysis on feedback surveys. Through analyzing employee feedback surveys and internal communication channels, businesses gain insights into the organization’s overall mood and satisfaction levels. This analysis helps HR departments identify areas of improvement, address concerns, and enhance the overall employee experience. Sentiment analysis contributes by enabling HR to gauge specific areas of concern but also the overall emotional well-being of employees. This facilitates more targeted and effective interventions, contributing to a positive workplace culture.
Approaches to sentiment analysis
As of 2024, a diversity of approaches persists in sentiment analysis. These divergent methodologies exhibit varying degrees of suitability contingent upon the specific contextual requirements.
Innovation leaders need to understand each approach before implementing any sentiment analysis system in their workplace.
Rule-based sentiment analysis
Rule-based sentiment analysis relies on predefined rules and lexicons to identify and categorize sentiments based on specific keywords and their associated scores. While it is simple to set up, scalability and nuanced expression capture can be challenging.
Hybrid sentiment analysis
Hybrid sentiment analysis combines rule-based and machine-learning approaches, aiming to optimize both speed and accuracy in understanding contextual intent within text. By leveraging the strengths of both methods, hybrid models can offer improved robustness for handling diverse language nuances.
Intent analysis
Intent analysis furthers sentiment analysis by discerning users’ underlying intentions behind messages. It goes beyond the basic positive, negative, or neutral classification, providing insights into whether a message conveys an opinion, news, marketing, complaint, suggestion, appreciation, or query.
Contextual semantic search (CSS)
Contextual Semantic Search is an intelligent algorithm that enhances sentiment analysis by filtering messages based on contextually similar concepts. Unlike traditional keyword-based searches, CSS improves accuracy by considering the contextual relationships between words.
Bag of Words analysis
Bag of Words Analysis is a traditional approach that represents text as an unordered set of words, disregarding grammar and word order. While simple, it may lack the ability to capture complex language structures and nuances.
RoBERTa-based analysis
RoBERTa, short for “Robustly optimized BERT approach,” is a transformer-based natural language processing (NLP) model. It is an extension and optimization of BERT (Bidirectional Encoder Representations from Transformers), designed to enhance the performance of language understanding tasks. These models consider contextual relationships between words, enabling a more nuanced understanding of language than a “bag of words” analysis.
Aspect-based sentiment analysis
Aspect-Based Sentiment Analysis focuses on specific aspects or features of a product or service, categorizing sentiments related to each element. This approach provides a detailed understanding of user sentiments towards different aspects, allowing businesses to address specific concerns effectively.
Sentiment analysis challenges
Sarcasm interpretation
Sarcasm and irony present a challenge for sentiment analysis. Given that the intention behind such statements is often incongruent with the literal meaning of the words, computational systems encounter difficulties in accurately interpreting sentiment within sarcastic sentences.
Negation handling
Negation is often highly dependent on context. Even for humans, negation is made more complicated by ambiguity in expression, variability in linguistic patterns, nuances in tone, positive negation complexities, and the dynamic evolution of language. These factors make accurately interpreting the sentiment of negated statements a complex task for algorithms.
Multipolarity
Multipolarity is when a sentence contains more than one sentiment. Although this is a common matter for humans, it’s pretty bewildering for machine learning models. It requires advanced techniques like aspect-based sentiment analysis to extract and understand each sentiment in such sentences.
Nuances in human communication
Machines struggle to grasp the subtleties embedded in language. This challenge arises from the contextual meanings of words, the influence of cultural nuances, and the intricacies of tone and expression. Machines find it difficult to discern the nuanced layers of human communication, including sarcasm, cultural references, and context-dependent variations in sentiment, leading to potential misinterpretations in sentiment analysis results.
Continuous evolution of language
The continuous evolution of language, characterized by introducing new words and expressions, presents a challenge for sentiment analysis models. As language dynamically adapts and incorporates novel terms, sentiment analysis algorithms may struggle to keep pace, potentially leading to misinterpretations or outdated sentiment classifications.
Emotion recognition
Emotional detection introduces complexity to sentiment analysis by requiring the interpretation of a range of emotions—joy, anger, sadness, regret—expressed through word choice. Recognizing and precisely categorizing these emotions pose persistent challenges for sentiment analysis models. The intricacies of human emotional expression make it difficult for machines to accurately capture and understand the full spectrum of sentiments conveyed in text.
Sentiment analysis tools
Today, third-party tools are the simplest way to implement sentiment analysis strategies. You don’t necessarily need your own sentiment analysis solution since many large and small companies offer these services.
Amazon Web Services
Through Amazon Comprehend, Amazon Web Services (AWS) offers a robust solution for real-time sentiment analysis.
Traditionally, such data is processed in batch mode, leading to delays in decision-making. With AWS, customer feedback can be analyzed within minutes, allowing businesses to react promptly to sentiment trends.
Use cases range from marketing departments refining campaigns based on customer segments to customer service departments addressing dissatisfaction promptly. The real-time sentiment analysis solution proves valuable for businesses seeking immediate insights into customer perceptions and sentiments.
Google Cloud Natural Language API
Google Cloud Natural Language API enables sentiment analysis, helping identify whether a given text is positive, negative, or neutral. This can be done directly with a text string or by analyzing a file in Google Cloud Storage.
Using the analyzeSentiment method, you can send a text and receive a response with sentiment details like magnitude and score. A positive score means positive sentiment, and a negative score means negative sentiment. The API also supports analyzing sentiment directly from files in Cloud Storage, making it convenient and powerful for understanding the emotional tone of text data.
Talkwalker
Talkwalker’s suite of tools includes AI-powered sentiment analysis to help businesses understand customer sentiments across various channels. This feature adds sentiment information to results from online reviews, social media, news articles, and survey responses.
By leveraging advanced sentiment technology, Talkwalker enables real-time risk management by flagging high-risk posts. The AI-powered analysis is designed to identify negative and sarcastic comments, allowing teams to respond promptly. The tool serves as a valuable asset for social media monitoring, providing insights into industry and customer trends on a large scale.
Idiomatic
Idiomatic, an AI-driven customer intelligence platform, offers tailored sentiment analysis that surpasses generic keyword approaches. It creates custom language models, providing in-depth insights into customer sentiments.
The platform interprets tone, categorizes sentiments, and enables faster, more accurate analysis than manual methods. Benefits include predictive analytics, improved customer satisfaction, and growth in positive sentiments. Idiomatic supports real-time analytics, integrates multiple data sources, and customizes AI models for business relevance.
Brand24
Brand24 offers AI-driven sentiment analysis as part of its internet monitoring tool. It helps businesses protect their reputation by identifying and addressing negative comments before they escalate. The tool categorizes mentions as positive, negative, or neutral, providing insights into customer opinions and enabling adjustments to meet their needs.
The sentiment analysis is based on advanced machine learning algorithms, ensuring human-like accuracy in over 90 languages. The tool also can understand sarcasm to a certain extent.
Uber utilized Brand24’s sentiment analysis to gather feedback about its new app and address insights quickly, as highlighted in a case study. The application extends beyond marketing, offering opportunities for content enrichment, predicting stock market fluctuations, and conducting various types of research. Businesses can sign up for free to leverage these capabilities.
Future Trends in Sentiment Analysis
Performing sentiment analysis will be an important procedure in the future of AI.
McKinsey has already predicted that marketing departments will most likely be heavily impacted by AI developments, as explained in the June 2023 analysis. Sentiment analysis will surely be a part of that.
In 2020, Bain and Co’s survey of CX tools called sentiment analysis a “relatively untapped source of customer data.”
As the capabilities for conducting sentiment analysis on pertinent datasets continue to expand, this is poised to become the standard practice.
In the coming years, sentiment analysis algorithms are anticipated to firmly establish their presence in sales and marketing, playing a pivotal role in client acquisition, customer engagement, and reputation management.
