What are your customer's thoughts on products or services? This is a question that every business needs to find answers to on a continual basis. The reviews that customers express about products/services are not mere words but a powerful communication that can either build a business or break it.
Customers' opinions about products and businesses have been increasingly visible since the emergence of social media and digital marketing. Online user feedback, such as reviews, social media comments, polls, surveys, and ratings, includes a wealth of information that cannot be ignored. This data reveals what consumers think of your product, what they like and hate about it, and, most crucially, how you can improve your product offering based on input derived from under the layers of feedback. Sentiment analysis may throw further light on these subjects and serve as a useful tool for analyzing your clients' moods and attitudes.
Sentiment analysis is growing more and more popular as technologies advance. In this regard, let's answer a few basic questions about what sentiment analysis is and how it can impact your business’ outlook.
What is Sentiment Analysis?
Sentiment analysis involves monitoring words written or spoken by an individual to determine the emotions they are most likely feeling at the time. Sentiment analysis technology analyzes a transcription of the conversation. The results of sentiment analysis offer businesses a better understanding of their customers.
Sentiment Analysis, also called Opinion Mining, analyzes and monitors languages and comments that quantify attitudes, views, and feelings about a business, product, or service. It categorizes pieces of writing or comments under labels such as confrontational, moderate, or optimistic.
An increasing number of businesses today are using sentiment analysis to examine direct communications. They can also analyze online communications of consumers across social media platforms or on online review sites.
Types of Sentiment Analysis?
People experience a wide spectrum of emotions – happy or sad, interested or indifferent, positive or negative. There are various algorithms available to capture this wide range of emotions.
Here are the most common forms of sentiment analysis:
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Fine-Grained
This form of sentiment analysis segregates comments into five categories – extremely positive, positive, neutral, negative, or very negative, thus providing an exact amount of polarity. Fine-grained sentiment analysis is most useful for studying ratings and reviews.
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Aspect-based
In evaluating the overall polarity of your customer reviews, the aspect-based analysis goes beyond fine-grained analysis. It helps you figure out which parts of the dialogue are being discussed. For example, a customer may write a review on a product claiming that the battery life is inadequate. The algorithm will then respond that the nasty feedback is about the battery life, not the product.
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Emotion Detection
Emotion Detection identifies emotions rather than positivity and negativity. Examples of emotions are anger, sorrow, happiness, frustration, fear, worry, and panic. Lexicons – collections of words that express distinct emotions – are widely used in emotion detection systems. Certain sophisticated classifiers also use robust Machine Learning (ML) techniques.
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Intent Analysis
Companies may save time, money, and effort by accurately detecting consumer intent. Businesses frequently find themselves pursuing customers who have no intention of purchasing soon. Accurate intent analysis can help solve this problem. The intent analysis can also assist in figuring out whether a customer intends to make a sure-shot purchase or is just window shopping.
How to conduct Sentiment Analysis?
As we dig further into understanding this powerful marketing and branding tool, let’s look at the steps applied in sentiment analysis.
Step 1: Data Gathering
First and foremost, we require the data to be analyzed. Scraping tools, APIs, customers' data feeds, and other methods can collect data from social media.
Step 2: Text Cleaning
By deleting stopwords (a, and, or, but, how, what), punctuation (commas, periods), and testing for stemming, text cleaning tools will allow us to process the data and prepare it for analysis. In addition, we will be able to ‘clean’ or ‘strip’ the texts of everything that isn't important to the analysis using these methods.
Step 3: Data Processing
After cleaning the data, the next step is to process the data. The processing of data depends on the kind of information it has text, image, video, or audio. It includes audio transcription, caption overlay, image overlay, logo recognition, and text extraction.
Step 4: Data Analysis
There are several subtasks to do in this stage of the sentiment analysis process. It includes training the model, multilingual data, custom tags, topic classification, and sentiment analysis.
Step 5: Data Visualization
The insights from data analyses are immediately translated into actionable reports in the form of graphs and charts when all the phases in the sentiment analysis process have been completed. These visual reports are extremely significant since they allow you to examine detailed, aspect-based outcomes. When you obtain an average score for your brand, for example, you can use the sentiment analysis dashboard to filter the findings to discover which features received high scores and which received poor scores. This will help you determine which regions require more attention than others.
Tools and Technologies for Sentiment Analysis
To harness the power of sentiment analysis and automatically sort social media data, businesses are connecting with sentiment analysis tools, thus allowing them to listen to the voice of the customer in real-time and prioritize negative comments. This further assists with improving brand reputation and preventing customer churn.
Best Sentiment Analysis Tools and Technologies
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Lexalytics
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MonkeyLearn
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Brandwatch
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Social Searcher
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Talkwalker's Quick Search
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MeaningCloud
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Clarabridge
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Rosette
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Repustate
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Aylien
How is Sentiment Analysis useful?
Sentiment Analysis is a technique to determine the emotional tone of texts. For example, it may determine whether a piece of writing has pleasant, fair, or unpleasant feelings.
It may help marketers to better comprehend client feedback and adapt their strategy as a result. It may also be used to analyze whether a specific campaign or product has a favorable or unfavorable impact on customers.
Market Research and Analysis:
Sentiment Analysis is a business intelligence approach used to identify the subjective reasons customers react a certain way toward something.
- Why do they buy a product?
- What do they think about the user interface?
- Does the product fulfill all their expectations and needs?
Likewise, it answers the questions listed above and helps examine opinions, trends, biases, reactions, and more in psychology and sociology.
Customer Service:
Customer service personnel frequently use Sentiment or intent analysis to automate the categorizing of incoming user emails into ‘urgent’ or ‘not urgent’ categories after gauging the email's sentiment, proactively detecting unhappy users. ML allows professionals to run such automation to help unravel emotions and intent.
Brand Monitoring:
One of the most well-known applications of sentiment analysis is to obtain a complete 360-degree perspective of how consumers and stakeholders perceive your brand, product, or organization. Widely available media, such as social media and product reviews across the web, provide insights into how well (or not) the product/organization is doing. Sentiment analysis also helps assess the impact of a new product, marketing campaign, or consumer's reaction to recent corporate news on social media.
Challenges in Sentiment Analysis
When it comes to the challenges in sentiment analysis, there are quite a few things that need to be taken into consideration. Organizations often struggle to obtain sentiment analysis accuracy. Sentiment analysis can be difficult in natural language processing because machines must be trained to analyze and understand human emotions as the human brain does. This is along with understanding the nuances of different languages. And with data analytics continuing to evolve, sentiment analysis software is better able to tackle these issues. Some of the main roadblocks in analyzing sentiment are as follows:
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Ambiguity and contextual understanding
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Subjectivity and variability
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Data quality and data noise
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Limited understanding of financial jargon
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Data availability
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Lack of truth labels
Where does sentiment analysis fail, and how to overcome it?
Even individuals face trouble interpreting their emotions effectively, rendering sentiment analysis among the most challenging tasks of Natural Language Processing (NLP).
Despite the improvements, data scientists still have a long way to go in creating more effective sentiment classifiers. Some primary issues that machine-based sentiment analyses face:
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Sarcasm Detection:
In sarcastic writing, people communicate their bad feelings with good phrases.
For example: "This laptop has an awesome battery backup of 2 hours."
Rule-based, statistical, ML algorithms and deep learning are different approaches for automatic sarcasm detection.
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Negation Detection:
Negation is a method of reversing the polarity of words, phrases, and even sentences. Researchers utilize a variety of linguistic principles to establish whether negation is taking place, but it is also vital to figure out what words are affected by negation terms.
For example, in the sentence “The movie was not interesting,” the scope is only the next word after the negation word. But for sentences like “I do not call this film a romantic movie,” the effect of the negation word “not” is until the end of the sentence.
The quality of a dataset for training and testing sentiment classification models inside negation will improve if samples with different types of reported negations are included.
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Word Ambiguity:
The difficulty of defining polarity in advance is an issue with word ambiguity since the polarity of some words is heavily reliant on the context of the phrase.
For example:
- “The storyline is unpredictable.”
- “The driving wheel is unpredictable.”
These two instances demonstrate how ‘the word emotion’ is influenced by context. The polarity of the word ‘unpredictable’ is anticipated to be positive in the first scenario. The polarity of the same word is negative in the second.
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Multipolarity:
Multipolarity can occur in a phrase, a document, or any other unit of text that we want to examine. In many circumstances, relying only on the study’s overall outcome can be deceiving, similar to how an average might conceal important information about all the statistics that went into it.
For example: “The display colors of my new tablet are so cool, but the audio quality is not that great.”
Some sentiment analysis methods will give this statement a negative or neutral valence. In such cases, a sentiment analysis model must assign a polarity to each part of the statement; in this case, ‘display’ has a positive polarity aspect, whereas ‘audio’ has a distinctly negative polarity aspect.
Future Trends in Sentiment Analysis
Despite the significant challenges, sentiment analysis in investment decision-making presents a plethora of future trends for advancement. They are as stated below:
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Advanced Natural Language Processing (NLP) Techniques: Natural language processing techniques are continuing to improve sentiment analysis framework. Techniques, including deep learning and contextual embeddings, are assisting models to detect complicated semantic nuances, thereby increasing sentiment classification accuracy.
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Integration of Alternative Data Sources: Integrating sentiment analysis with other data sources will enable businesses to gain further insights into market sentiment. With this, investors can also analyze sentiment in a larger context, thereby enhancing the reliability and accuracy of sentiment-based investment decisions.
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Real-Time Analysis: By integrating real-time sentiment research, investors can capture and respond to evolving market sentiments. This real-time research will also provide a competitive advantage.
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Integrating Sentiment Analysis with Different Analytical Tools: By using sentiment analysis along with other analytical methods like predictive modeling and network analysis, businesses can gain a more holistic approach to investing decision-making. This will help capitalize on each technique’s strengths and offer a more robust framework for investment.
Importance of Sentiment Analysis in Modern Businesses
In a natural language processing (NLP) procedure, sentiment analysis is used to determine if the selected information is good, pessimistic, or impartial. It is performed on text-based information to enable organizations to check brand and item opinions in customer feedback.
Sentiment analysis is not a device that organizations can use for customer communications. It is an additional asset that assists them with branding management in virtual entertainment. Customer sentiment can help in developing customer devotion and maintenance by presenting results and upgrading the customer experience. The significance of customer sentiment helps in understanding the good or pessimistic opinions the customer communicates straightforwardly to the association.
Key Takeaways
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Sentiment analysis assists in scanning words written or said by an individual to determine the emotions they are likely feeling at the time.
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Organizations can monitor real-time conversations about their products or services and measure consumer sentiment.
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The data from sentiment analysis can be used to understand which products or services the customers want or how they feel about a brand.
In a nutshell
Sentiment analysis may be applied in various business contexts, such as brand monitoring, product analytics, customer service, and market research. Leading businesses can work quicker, more accurately, and toward more valuable purposes by embedding sentiment analysis tools into their existing data systems.
Resultantly, decision-makers can receive fresh insights, gain a deeper understanding of customers, and more effectively empower employees to meet their targets.
SG Analytics, recognized by the Financial Times as one of APAC's fastest-growing firms, is a prominent insights and analytics company specializing in data-centric research and contextual analytics. Operating globally across the US, UK, Poland, Switzerland, and India, we expertly guide data from inception to transform it into invaluable insights using our knowledge-driven ecosystem, results-focused solutions, and advanced technology platform. Our distinguished clientele, including Fortune 500 giants, attests to our mastery of harnessing data with purpose, merging content and context to overcome business challenges. With our Brand Promise of "Life's Possible," we consistently deliver enduring value, ensuring the utmost client delight.
Frequently Asked Questions (FAQs) - Sentiment Analysis
1. How can businesses benefit from real-time sentiment analysis?
By using sentiment analysis tools, businesses can measure how potential consumers perceive their brand. Analyzing social media and survey data can help in gaining essential insights into how the brand is performing. Businesses can also gauge how their services are being received by their target market.
2. What are the limitations of sentiment analysis?
Sentiment analysis methods often fail to identify grammar mistakes, sarcasm, negation, misspellings, or irony. Therefore, it may not be suitable for analyzing data gathered from different social media platforms.
3. Are there any privacy concerns with sentiment analysis?
Sentiment analysis raises certain ethical and legal concerns, including privacy, transparency, consent, bias, or manipulation. These concerns are critical for customer service teams to comply with relevant regulations and policies related to data collection, processing, and storage.
4. What industries benefit the most from sentiment analysis?
Sentiment analysis benefits several industries, including healthcare, banking, aviation, retail, etc. It further aids companies to better their services and products for an enhanced market share.
