What Is Sentiment Analysis?

Flipnode on May 29 2023


With the rise of social networks and digital marketing, the opinions of customers regarding products and brands have become increasingly visible. Online user feedback, including reviews, social media comments, and surveys, contains a wealth of valuable data. This data holds the potential to offer insights into customer perceptions of your product, their preferences and concerns, and, most importantly, how to effectively respond to their feedback. Sentiment analysis emerges as a valuable tool in uncovering these insights and managing your brand's reputation by analyzing the sentiments and opinions expressed by your customers.

This article will delve into the significance of sentiment analysis for online-based businesses, explore its primary approaches, and emphasize the role of machine learning (ML) and natural language processing (NLP) in this domain.

Sentiment analysis explained

Sentiment analysis, also known as opinion mining, is an automated approach that involves identifying, extracting, quantifying, and studying attitudes and opinions expressed towards a brand, product, or service. This method relies on various techniques such as natural language processing (NLP), computational linguistics, machine learning, and other tools. By applying sentiment analysis, it becomes possible to assign sentiment scores to specific entities within a written sentence and determine whether the sentiment expressed is positive, negative, or neutral.

This automated approach enables businesses to analyze a large volume of customer reviews and social media data, gaining insights into how customers perceive the brand and its products. It helps determine customer satisfaction levels with pricing conditions and customer service. Consequently, brands can gain a deeper understanding of public opinion, conduct comprehensive market research, and monitor reviews. These measures, in turn, facilitate businesses in adapting to their customers' needs and tailoring their products accordingly.

Types of sentiment analysis

Sentiment analysis models are designed to categorize customer feedback into different aspects, such as polarity (positive, neutral, negative), emotions (disappointed, happy, furious), intentions (interested or not, willing to buy or not), and urgency. Depending on your analysis goals, you can customize the categories to suit your specific needs. Here are some popular types of sentiment analysis:

  • Fine-grained sentiment analysis: This approach aims to provide precise sentiment analysis by including additional polarity categories, such as very negative, negative, neutral, positive, and very positive. These categories can be aligned with five-star rating reviews, where very positive corresponds to 5 stars and very negative corresponds to 1 star.
  • Emotion detection: This type focuses on identifying specific emotions and feelings expressed in the text, such as frustration, happiness, and others. Emotion detection methods often utilize lexicons or systems of emotionally charged words. Machine learning algorithms can also be employed to detect sentiment based on certain words or patterns.
  • Aspect-based sentiment analysis: Brands often want to understand which specific features or aspects of their products customers are discussing in a positive, negative, or neutral manner. For example, in the review "The camera in this phone is worse than I expected," a negative sentiment is expressed specifically towards the camera feature.
  • Multilingual sentiment analysis: This type allows for evaluating sentiment scores in texts written in different languages. Cultural and linguistic variations influence the choice and usage of words to express emotions and thoughts. Therefore, literal translations may not capture the intended meaning accurately. Multilingual sentiment analysis takes into account the nuances of different languages, providing precise interpretations without relying on word-for-word translations.

Why is sentiment analysis important?

Sentiment analysis enables the efficient sorting and analysis of vast amounts of social media conversations and reviews by using automated methods. This capability allows companies to make well-informed decisions based on comprehensive data and in-depth analysis.

Here are some ways in which basic sentiment analysis facilitates the gathering and measurement of social data:

  • Handling large data volumes: The exponential growth of online data presents a challenge for manual collection and processing. Sentiment analysis tools enable companies to process massive amounts of data, as the volume of online data is expected to be enormous. For instance, it was projected that by 2020, online data would reach 44 zettabytes, which is 40 times more than the number of stars in the observable universe.
  • Real-time analysis: Staying up to date with customers' opinions and reactions in real time is crucial. With sentiment analysis, companies can monitor and analyze sentiment as it emerges, enabling them to take immediate action when faced with significant issues or opportunities.
  • Centralized analysis criteria: Determining whether a piece of text expresses a positive, neutral, or negative sentiment can be subjective and influenced by personal experiences and beliefs. By using a unified sentiment analysis system, companies can apply consistent criteria to analyze all text data. This ensures a more objective and standardized approach to sentiment analysis.

How does sentiment analysis work?

To gain a deeper understanding of sentiment analysis, it is essential to explore the main approaches employed in this field. There are three major sentiment analysis algorithms commonly used: rule-based (lexicon-based), automatic (machine learning), and hybrid approaches.

Rule-based approach:

Rule-based sentiment analysis algorithms primarily rely on manually crafted rules to determine polarity, subjectivity, and sentiment in a given text. These rules are based on various NLP sentiment analysis techniques developed in computational linguistics, including part-of-speech tagging, tokenization, stemming, and more.

In this approach, sentiment analysis utilizes sentiment analysis datasets, which consist of extensive libraries of adjectives (such as good, fantastic, disgusting, and terrible) and phrases (like excellent service, awful movie) that have been previously assigned specific scores by humans coders.

However, the process of assigning sentiment scores manually can be challenging and prone to inaccuracies. Agreement among coders is crucial, as discrepancies can lead to confusion and incorrect results. For example, if one person assigns a sentiment score of 0.5 to the word "good," while another person gives the same score to the word "amazing," the sentiment analysis system will perceive both words as equally positive, resulting in flawed outcomes.

Here's an example of how a rule-based sentiment analysis system operates:

  1. Determines two polarities using separate lists of polarized and sentiment-bearing words (e.g., negative words like horrible, bad, awful, and positive terms like best, good, fabulous).
  2. Assigns a sentiment score to each word and phrase.
  3. Counts the occurrences of positive and negative words in the text.
  4. If the number of negative words exceeds the number of positive words, the system returns a negative sentiment, and vice versa. If the counts are equal, the sentiment is marked as neutral.

While the rule-based algorithm is relatively straightforward to implement and comprehend in terms of the guiding rules, it has limitations. It oversimplifies complex word combinations and requires additional rules to enhance accuracy, necessitating ongoing investment in development and maintenance.

NLP and ML-based sentiment analysis

The automatic sentiment analysis method relies on machine-learning algorithms that are trained on provided data.

What is natural language processing (NLP)?

Natural language processing is an interdisciplinary field encompassing linguistics, computer science, and machine learning. Its main objective is to understand how machines can comprehend and interpret human language. NLP involves analyzing semantic, syntax, and contextual information to extract meaning from text.

Machine learning plays a crucial role in NLP and text sentiment analysis by automating and enhancing low-level text analysis tasks such as part-of-speech tagging, tokenization, and sentiment identification. For example, machine learning experts can train a model to recognize verbs by exposing it to a large dataset containing pre-tagged examples. By utilizing machine learning techniques like neural networks and deep learning, the model learns the patterns and characteristics of verbs.

The learning process initially involves a semi-automated approach. The algorithm learns to identify and analyze sentiment by leveraging the provided data. Training continues until the sentiment analysis model achieves a sufficient level of autonomy and accuracy to effectively analyze unfamiliar texts.

NLP and sentiment analysis can employ both supervised and unsupervised machine learning techniques.

Supervised machine learning for sentiment analysis

Supervised machine learning-based sentiment analysis involves training a statistical model with pre-tagged texts for analysis. Once trained, the model can analyze untagged examples. Popular supervised NLP machine learning algorithms include Bayesian Networks, Support Vector Machines, and Conditional Random Field.

Key aspects of supervised machine learning in sentiment analysis include:

  • Tokenization: Breaking text documents into smaller units, such as words, to enhance model understanding.
  • Part-of-speech tagging: Identifying different parts of speech, such as nouns, verbs, and adjectives.
  • Sentiment analysis: Determining whether the text is positive, negative, or neutral and assigning sentiment scores to individual entities.

On the other hand, unsupervised machine learning for sentiment analysis involves training models without pre-tagging. Techniques such as clustering are utilized to group similar text, while latent semantic indexing (LSI) aims to identify word and phrase associations within sentences.

Although unsupervised machine learning may have limitations, combining multiple approaches and techniques can maximize performance.

The main distinction between automatic ML-based approaches and rule-based approaches is that the former can handle larger volumes of data due to automation. However, the ML-based algorithm may struggle to explain why specific texts are classified with positive or negative sentiment.

In general, to achieve the highest accuracy, a hybrid approach that combines lexicon-based sentiment analysis techniques with ML algorithms is recommended.

Sentiment analysis challenges

Sentiment analysis poses significant challenges in NLP, as even humans struggle to accurately identify and analyze sentiment. While sentiment analysis models are improving in accuracy, several obstacles hinder them from being the ultimate solution.


Words spoken or written always have specific circumstances, timeframes, and individuals involved. Context plays a crucial role in understanding sentiment, but machines struggle to recognize it without explicit cues. For instance, consider two responses to a conference survey:

  1. All of it.
  2. Totally nothing.

If these responses answer the question "What did you dislike about the conference?" the first response conveys negative sentiment, indicating a dislike for everything, while the second response expresses positive sentiment, suggesting a liking for everything. However, if the question changes to "What did you like about the conference?" the sentiment behind these responses reverses.

To capture sentiment accurately, understanding context is necessary. Teaching models to comprehend context is a complex and non-straightforward process.


Deciphering comparative sentences requires a deep understanding of the compared objects to discern sentiment. The same applies to sentiment analysis systems. Consider the example:

Those headphones are like concert speakers!

Without explicit emotions, an analysis system would struggle to determine if this sentence is positive, neutral, or negative. It needs to know that concert speakers are loud and headphones are silent, thereby implying a positive sentiment.


Tone in writing reflects the creator's attitude toward the subject and is conveyed through word choice. Training machines to understand tone presents challenges. Let's examine a customer's review:

I knew it would be difficult to integrate their service into my infrastructure, but I didn't think it would be that hard. Everything their support did was hopeless, and even the manager was clueless. We spent countless hours on the phone with no resolution. It turns out my infrastructure was faulty. I managed to take care of that and accommodate their product. I am very thankful for the patience of their technical support!

To humans, it's evident that the customer has a positive sentiment toward the company and its service. However, using a rule-based approach, sentiment analysis software would count negative and positive words to determine sentiment. In this case, with seven negative words and one positive word, the software would categorize the sentiment of this review as negative.

Sarcasm and Irony

Sarcasm and irony often employ positive words, making it challenging for machines to understand sentiment without contextual knowledge. For example, on a travel company's website, reviews answering the question "Did you enjoy traveling with us?" can be sarcastic:

  1. Absolutely, the best travel agency ever!
  2. Sure, the experience I got was unforgettable!

At first glance, these responses may seem positive due to words like "best" and "sure," typically associated with positivity. However, they can also be interpreted sarcastically, conveying negative sentiment. Multiple situations can lead to such interpretations.


Emojis, including Western and Eastern emojis and Unicode characters, are vital in sentiment analysis, particularly for interpreting tweets. Analyzing emojis and characters is as crucial as analyzing words and other linguistic components. Emojis can be tokenized and whitelisted to enhance sentiment analysis performance.

Other challenges include subjectivity, tone, human annotator accuracy, and comparisons. Despite rapid advancements in machine learning, resolving these issues requires substantial time and effort.

Sentiment analysis applications

Sentiment analysis finds application in various domains, including brand monitoring, market research, and customer service. Let's explore some of the key use cases:

Brand monitoring

By analyzing sentiment in blogs, forums, news articles, and other sources, you can understand customer opinions and perceptions of your brand. Aligning sentiment analysis with specific production and development cycles, such as marketing campaigns or product releases, allows you to track customer satisfaction metrics and observe how your brand reputation evolves over time compared to competitors. Real-time sentiment analysis enables proactive identification of potential reputational crises, enabling timely intervention to mitigate issues.

Market research

Sentiment analysis is highly beneficial in any market research endeavor, whether it involves studying competition or exploring new markets. For instance, analyzing online reviews of your competitors' new products can provide insights into their strengths, weaknesses, and opportunities to learn from their strategies. Tracking your brand and competitors on social media in real-time helps you identify emerging trends and adapt to evolving customer demands.

Customer service

In customer service, instant and seamless interactions are crucial for customer satisfaction. The manner in which companies deliver products and services is as significant as the offerings themselves. Customer sentiment analysis can be leveraged to prioritize incoming queries based on urgency and topic, efficiently routing them to the relevant departments. This approach streamlines communication with customers, ensuring that time-sensitive matters are promptly addressed.


With the increasing volume of customer reviews and online comments generated every day, the need to efficiently process this data and extract valuable insights becomes evident. Sentiment analysis plays a crucial role in understanding customer sentiments towards your brand and products, enabling you to enhance your services accordingly. Leveraging natural language processing and continuously evolving machine learning techniques, sentiment analysis serves a wide range of applications, including brand monitoring and market research.

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