What is Sentiment Analysis? Definition, Tools, and Applications

Sentiment analysis is defined as a field of study that uses computational methods to analyze, process, and reveal people’s feelings, sentiments, and emotions hidden behind a text or interaction. This article explains sentiment analysis, its working, usefulness, and the top five sentiment analysis tools.

What is Sentiment Analysis?

Sentiment analysis is a field of study that uses computational methods to analyze, process, and reveal people’s feelings, sentiments, and emotions hidden behind a text or interaction. It uses machine learning (ML), natural language processing (NLP), data mining, and artificial intelligence (AI) techniques to mine, extract and categorize users’ opinions on a company, product, person, service, event, or idea for various sentiments.

Sentiment analysis allows organizations to gain insights into the vast volumes of unstructured data from various online sources such as social media, emails, chats, blogs, and forums. According to a 2019 study by CIO, unstructured information accounts for around 80-90% of all digital content.

Fundamentally, sentiment analysis algorithms undertake either of these three approaches: rule-based, automatic and hybrid for data processing. As the name suggests, the rule-based approach uses predefined and lexicon-based manually crafted rules to classify sentiments. The automatic approach employs machine learning methods, while the hybrid approach uses a combination of the above to perform sentiment analysis.

Sentiment analysis also referred to as opinion or sentiment mining, captures the polarity of the text, which often falls under the categories of positive, negative, or neutral. Moreover, associating sentiments and emotions with text runs across different levels, such as sentences, paragraphs, and documents.

Several companies use sentiment analysis tools to streamline and optimize their businesses based on the volatile and constantly changing market, customer opinion, and feedback. Companies offering sentiment analysis tools or SaaS products include Zoho, Lexalytics, and Brandwatch, to name a few.

Types of sentiment analysis

Sentiment analysis models are designed to capture and classify a variety of sentiments, including happiness, sadness, surprise, interest, joy, anger, and others. It is primarily divided into four types:

1. Fine-grained

The fine-grained type allows you to define the polarity of the text or interaction precisely. Polarity implies sentiments ranging from positive, negative, or neutral to very positive or very negative. Customer reviews and ratings are analyzed under this category. For example, considering the rating 1-10 implies that the rating 1-4 may denote a negative sentiment while a rating 5-10 shows a positive sentiment.

2. Aspect-based

Aspect-based sentiment analysis goes a step further as it analyzes specific aspects that users discuss about a product, service, or idea. For example, let’s say a customer gives a review for a laptop, stating, “The webcam seems to go on and off randomly”. In this case, with aspect-based analysis, the laptop manufacturer can understand that the customer has made a ‘negative’ comment on the ‘webcam’ component of the laptop.

3. Emotion detection

Emotion detection determines emotions such as joy, sadness, fear, worry, etc. It uses lexicons (set of words and expressions) that identify specific emotions and machine learning-based classifiers. As humans express feelings in various ways, ML-based emotion detection is preferred over lexicons.

For example: “This phone is just insane”. Such a review may confuse the sentiment analysis model as it may evoke two different sentiments. One may be entirely positive, while the lexicon ‘insane’ may classify it as one denoting fear or panic. Thus, it may give inaccurate results if only lexicons are used. However, with ML-based detection, such a possibility is avoided.

4. Intent analysis

Consumer intent is an essential variable that businesses must tap into while channeling their efforts to save time and money. Intent analysis helps accomplish this task by identifying user intent–whether the user is interested in purchasing a product or is just browsing the website without any intention of buying one.

Consumers intending to buy the product can be tracked down and facilitated with targeted advertisements. Those who do not intend to purchase the product can be left alone, thereby saving the costs, efforts, and resources put into advertising.

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How Does Sentiment Analysis Work?

Sentiment analysis predominantly uses NLP and ML to make sense of the linguistic nuances observed in user interactions. The foundations of sentiment analysis are laid by the developers who design a machine learning algorithm capable of detecting content having varied sentiments.

In the first step, input data or text is processed with the help of ML classifiers, which train large volumes of datasets that contain positive, negative, and neutral sentiments. In this phase, the data is divided into fundamental text components such as words, phrases, and sentences.

Let’s take a look at a typical ML classifier:

In the training phase, input text goes through the feature extractor, which extracts features to generate feature vectors, labels, and tags (positive, negative, or neutral). Feature extraction methods based on word embeddings or word vectors give words with similar meanings a similar representation. The generated vectors are then inputted to the ML algorithm that produces a classifier model.

Later, in the prediction phase, new data is fed into the feature extractor to generate feature vectors, which the classifier model then processes to predict the sentiments hidden in the new input data. Classification algorithms such as Naïve Bayes, linear regression, support vector machines, and deep learning are used to generate the output.

The AI model provides a sentiment score to the newly processed data as the new data passes through the ML classifier. For example, 1 may represent a negative sentiment, 0 may denote neutral, and +4 may express positive opinion.

Let’s consider an example:

“I really loved the Toshiba Satellite Pro C40-B I0016 laptop. Although the webcam has issues, it is rare to find such a high-end laptop at that price”.

• Toshiba Satellite Pro C40-B I0016 laptop…loved = +4
• webcam…issues…= -2
• it is rare…high-end laptop…that price = +3

Once sentiment scores are assigned, the score of each component is summed up, generating the final score of the entire statement that reveals the overall sentiment. In the above example, the prevailing opinion is positive; however, negative sentiment toward the webcam can be observed.

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How is Sentiment Analysis Useful?

Sentiment analysis is a robust process that allows organizations to derive insights from unstructured data and reinvent business strategies that are most compelling to the target market.

Let’s understand the use of sentiment analysis for different organizations:

1. Offers integrated analysis

Integrated sentiment analysis links sentiments to user behavior, where the behavior is further associated with demographics, transactions, and even specific user profiles that highlight a specific emotional aspect. Once the link is established, one can offer similar profiles the same service.

For example, corporate companies can use employee data of individuals who have left the organization to understand their feelings toward their colleagues, managers, and the company. This allows them to understand and correlate the similarities in the employee profiles that have raised the attrition issue. Moreover, the company can use integrated sentiment analysis to make changes in the company’s culture, employee policies, and constraints to lower the attrition rate.

2. Efficient brand monitoring practice

Companies use social media data to determine customer response to a product or service. With this, organizations can focus on improving a product as they understand user sentiments on a certain product quality that is most liked and preferred by all.

In situations where strong words are used against a brand or product, such as ‘unhappy, very disappointed’, companies can prioritize and take immediate action to avoid further damage to the brand. Moreover, they also use sentiment analysis to compare how their products are performing in light of their competitors’ products. Thus, brand monitoring allows organizations to monitor different web or social media channels and fine-tune or alter their business strategies.

3. Develop and refine public relations (PR) strategies

Organizations use sentiment analysis as a metric to strategize, plan, and implement PR strategies. Several firms apply analysis to their customer care unit to better understand customer grievances and the need to improve certain PR aspects. For example, industry and market trends can provide sales leads through sentiment analysis.

Moreover, social media influencers can be tracked and identified with sentiment analysis that may evoke positive sentiments for a brand. It thereby becomes a vital cog in devising a successful PR strategy.

4. Product or service marketing

With the launch of a new product, companies can employ sentiment analysis to understand user response to the new product rather than putting efforts into conducting surveys for the same. Customer reviews and ratings observed on ecommerce sites such as Flipkart and Amazon are an efficient way of flagging positive and negative sentiments toward a product.

Based on customer feedback, companies can zero in on speeding up the product production process, identify the features that need to be added, resolve bugs from elements causing problems, and so on. Thus, sentiment analysis is an effective tool for product or service marketing.

5. Efficient data mining practice

Businesses can use sentiment analysis as a data mining tool that can help them gather competitive intelligence concerning competitor brands, thereby contributing to the business’s competitive advantage. Neural networks play a crucial role in such data mining processes.

For example, brands can monitor and keep track of their social media mentions (social listening) with comments about their rivals. The analysis of such data gives an idea of consumer sentiments toward the products of competing brands. With such comparative data, companies can gain a competitive edge over other brands, allowing them to tweak or adjust their business model based on market sentiments.

6. Supports political analysis

Sentiment analysis on social media platforms such as Twitter can allow official authorities to keep a check on people’s reactions to newly-framed political policies. Political parties can reframe their policies and plan their election manifesto or campaigns based on people’s responses, anger, and common trends.

Similarly, opposition parties can monitor public support for new laws and then use them to define their agendas. Moreover, the party or leader that is highly probable to sweep to victory in the elections can be forecasted through sentiment analysis.

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Top 5 Tools of Sentiment Analysis

Sentiment analysis tools refer to applications that dive deeper into user interactions to extract the intent, emotion, and tone behind them and derive more context from user conversations. Such tools come in handy for companies that use social media platforms, chats, or emails to engage with their consumers.

These are the top five sentiment analysis tools used by companies:

1. Brandwatch

Brandwatch is a popular sentiment analysis tool that keeps track of various social media aspects to reveal the user sentiment towards a service or brand.

Today, companies typically use Brandwatch to analyze a user’s voice 24/7 and notice the subtle changes in the user’s sentiments toward a particular brand. It has a distinct feature of ‘image insights’, which allows you to consider an image as input data in addition to text data. Moreover, it permits you to monitor logos shared over various digital platforms.

The tool allows you to watch platforms where your brand images surface. You can also maintain a record of your brand’s performance for a specific target audience based on the customers’ emotions, tones, and attitudes.

The tool offers a free trial for 15 days and is supported by social media platforms such as Facebook, LinkedIn, Twitter, etc.

Pricing: Available on request

2. Talkwalker

Talkwalker keeps a close eye on social media content to perform sentiment analysis. It offers a ‘Quick Search’ feature that analyzes user comments, interactions, brand mentions, blogs, and chats to record their reactions to your activities and posts on social media.

Talkwalker uses artificial intelligence to study user sentiments and also supports 25 languages. Moreover, you can monitor user trends and identify popular and not-so-popular features through sentiments.

The tool provides a free trial for 15 days and is supported by Facebook, Instagram, Twitter, etc.

Pricing: Starts at $800 per month

3. Awario

Awario is a sentiment analysis tool that monitors social media to determine user sentiments and mentions in real-time. It is best suited for companies or individuals who are used to handling figures and numbers. The tool provides an interactive user interface that categorizes sentiments based on brand, topic, and keywords. Moreover, the dashboard shows the negative feedback for your rivals or competitors.

Awario covers most online sources, including various websites, blogs, forums, and social media platforms. The tool offers a 14-day free trial and is supported by Facebook, Instagram, Twitter, etc.

Pricing: Starts at $29 per month

4. Lexalytics

Lexalytics is a unique sentiment analysis tool capable of giving insights into why a user responds to the provided service in a certain way. It uses NLP in the initial stages to process text and then performs the task of sentiment analysis to identify the emotions hidden in a user’s interaction. Lexalytics can configure text analytics, process large quantities of text input, and provide security by running the tool behind a firewall.

Moreover, Lexalytics provides a user-friendly and easy-to-read display that one can share between devices or users. Fundamentally, most sentiment analysis tools offer insights into how users feel about something; however, the Lexalytics tool answers the question ‘why’.

Pricing: Available on request

5. Clarabridge

Clarabridge offers a customer experience management solution to detect user sentiments across customer service departments (CX Analytics) and social media networks (CX Social). Typically, it tracks emails, surveys, chats, and more to provide clear insights into all your user sentiments.

The tool provides speech analytics that allows audio data processing for sentiment analysis and enables corporate teams to manage calls and customer support better. It uses an 11-point scale to label or classify user sentiments based on words and grammatical subtlety.

The tool offers a 14-day free trial and is supported by social media platforms such as Facebook, Instagram, Twitter, etc.

Pricing: Available on request

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Takeaway

Sentiment analysis is a rapidly-evolving field of computational science. Today, companies looking to scale new heights in the market need a better idea of how their products are performing, if the consumers are satisfied, how to outpace competitors, and what factors influence buyers before they buy any entity.

All these requirements call for considering sentiment analysis in the organizational framework. Moreover, the technology replaces traditionally prevalent processes such as door-to-door or telephonic surveys that gather insights into consumers’ tastes, market trends, and overall company performance.

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