With the advent of social networks and digital marketing, customers’ opinions about products and brands have become increasingly visible. User feedback online, such as reviews, social media comments, and surveys, contains tons of valuable data. This information may provide insight into what customers think about your product, what they like and dislike, and, most importantly, how to react to their feedback. Sentiment analysis can shed more light on these topics and become a helpful tool to analyze the moods and opinions of your clients, as well as manage the reputation of your brand.
This article will focus on sentiment analysis and its importance for online-based businesses, its main approaches, and the role of machine learning (ML) and natural language processing (NLP) in it.
Sentiment analysis, also often referred to as opinion mining, is an automated method used to identify, extract, quantify, and research attitudes and opinions towards a brand, product, or service. This method relies on NLP, computational linguistics, machine learning, and other tools. It helps allocate sentiment scores to the entities within a written sentence and determine positive, negative, or neutral sentiment in the text.
This automated method allows businesses to analyze a large number of customer reviews and social media data to understand how customers feel about the brand and its products, whether they are satisfied with pricing conditions and customer service. This way, brands can gauge public opinion, conduct detailed market research and review monitoring. All these measures, in turn, help businesses adjust to their customers’ needs and tailor their products correspondingly.
Sentiment analysis allows businesses to analyze large numbers of customer reviews online
Sentiment analysis models aim at defining 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 use various categories to interpret customer feedback and adjust them to your specific needs. Some of the most popular sentiment analysis types include:
If you seek to make your sentiment analysis as precise as possible, you can add additional polarity categories, such as:
These categories correlate with five-star rating reviews, where very positive is equal to 5 stars and very negative is equivalent to 1 star.
This type focuses on emotions and feelings, e.g., frustration, happiness, and others. Many of the emotion detection approaches are lexicon-based, meaning they use systems of emotionally charged words. You can also use machine learning algorithms to detect the sentiment behind certain words.
When analyzing sentiments in a piece of text, brands want to know what specific features and aspects of their products customers are discussing in a positive, negative or neutral way. For example, in this review: “The camera in this phone is worse than I expected,” a negative opinion is expressed towards a particular feature of the product.
Since sentiment analysis uses automated methods, it makes it possible to sort out and analyze enormous amounts of the sentiment behind social media conversations and reviews in a timely manner. As a result, companies can make better and more informed decisions based on sufficient data and in-depth analysis.
Overall, basic sentiment analysis facilitates the process of gathering and measuring social data in several ways:
Seizing large amounts of data. According to the World Economic Forum, it was expected that the amount of data online was going to reach 44 zettabytes by 2020, which is 40 times more bytes than the stars in the observable universe. These statistics are both stunning and intimidating since there’s no way to collect and process this data manually. Therefore, you would need automated sentiment analysis tools.
Real-time analysis. It is always crucial to stay updated on your customers’ opinions and reactions in real time to take action immediately if a severe problem arises.
Centralized analysis criteria. Deciding on whether a piece of text is positive, neutral, or negative can be a challenging task for humans since they may make subjective judgments based on their previous experiences and beliefs. That is why it is better to be guided by a unified sentiment analysis system that can be applied to all text data.
To understand how sentiment analysis works, we need to dig deeper into the main approaches it employs. There are three major sentiment analysis algorithms that can be implemented in sentiment analysis and opinion mining: rule-based (lexicon-based), automatic (machine learning), and hybrid.
Most of the time, rule-based sentiment analysis algorithms rely on manually crafted rules to determine polarity, subjectivity, and sentiment in a piece of text. These rules are based on different NLP sentiment analysis techniques that were initially developed in computational linguistics, including part-of-speech tagging, tokenization, stemming, etc.
In this approach, sentiment analysis makes use of sentiment analysis datasets, e.g., large libraries of adjectives (good, fantastic, disgusting, terrible) and phrases (excellent service, awful movie) that have been previously assigned particular scores by human coders.
This hand-scoring process can be tricky and inaccurate since everyone participating in it has to come to an agreement regarding the sentiment scores. For instance, if one person assigns a sentiment score of 0.5 to the word good, but another person gives the same sentiment score to the word amazing, your sentiment analysis system will perceive both words as equally positive, which will lead to subsequent confusion and wrong results.
Let’s take a look at an example of how a rule-based sentiment analysis system works:
Determines two polarities with two lists of polarized and sentiment-bearing words, e.g., negative words such as horrible, bad, awful, and positive mentions such as best, good, fabulous, etc.
Attaches a sentiment score to each word and component.
Counts how many times positive and negative words appear in the text.
If the number of negative words is bigger than the number of positive words, the system returns a negative sentiment and vice versa. If the numbers are equal, the total sentiment will be marked as neutral.
-1 = Negative / +1 = Positive
The rule-based algorithm is easy to implement and clear in terms of the rules guiding the analysis; however, it’s too simplified and not capable of dealing with more complex word combinations. This algorithm needs additional rules to make it more accurate, which requires constant investment to maintain development.
The automatic sentiment analysis method is based on machine-learning algorithms and is being trained on the data fed to it.
Natural language processing is a study field at the intersection of linguistics, computer science, and machine learning. Its main focus is to analyze how machines interpret natural human speech. In NLP, semantic, syntax, and context information needs to be analyzed in order to extract meaning from a piece of text.
The primary role of machine learning in NLP and text sentiment analysis is to enhance and automate the low-level text analysis functions, such as part-of-speech tagging, tokenization, sentiment identification, and others. For instance, machine learning specialists can train a model to determine verbs by giving it a large number of texts with pre-tagged examples. The model will learn what verbs look like using such machine learning techniques as neural networks and deep learning.
The learning starts as a semi-automated process. The algorithm learns to recognize and analyze sentiment based on data provided to it. The training continues until the sentiment analysis model reaches a certain level of autonomy and accuracy, sufficient to analyze unfamiliar texts correctly.
NLP and sentiment analysis may involve supervised and unsupervised machine learning.
Natural language processing focuses on text data and helps extract meaning from it
In supervised ML-based sentiment analysis, a statistical model is fed a number of pre-tagged texts to analyze. After the training, the model is given un-tagged examples to analyze. Some of the most popular supervised NLP machine learning algorithms are Bayesian Networks, Support Vector Machines, Conditional Random Field, etc.
All in all, supervised machine learning involves:
Tokenization – breaking text documents into smaller pieces, such as words, for the model to better understand.
Part-of-speech tagging – identifying parts of speech, e.g., nouns, verbs, adjectives.
Sentiment analysis itself – identifying whether the piece of text is positive, negative, or neutral and giving a specific sentiment score to each entity.
In unsupervised ML, a model trains without any pre-tagging. It uses such techniques as clustering, that is, grouping similar text together, and latent semantic indexing (LSI), which aims at identifying words and phrases that often appear next to each other in sentences.
Unsupervised machine learning can be flawed; that’s why the best solution, as always, is to combine several approaches and techniques to achieve maximum performance.
The main difference between the automatic ML-based approach and the rule-based one is that the former can analyze way more data due to the automatization. The disadvantage of the ML-based algorithm is that it makes it difficult to explain why specific texts are categorized as bearing positive or negative sentiment.
In general, to achieve the highest accuracy, it is better to use a hybrid approach, which combines lexicon-based sentiment analysis techniques with ML algorithms.
Sentiment analysis is one of the most challenging jobs in NLP since even people may struggle to identify and analyze sentiment correctly. Even though sentiment analysis models are getting more superior and accurate, there are still numerous obstacles that prevent them from being the ultimate solution.
All spoken and written words are uttered in some specific circumstances, at some point in time, by some particular people and to other people. In other words, they all have context behind them. The problem is that machines cannot recognize the context if it isn’t brought up on purpose. Let’s imagine a situation where we have two responses to a survey regarding a recent conference:
All of it.
Now suppose, these two responses answer the question “What did you dislike about the conference?” In this case, the first answer would bear negative sentiment, meaning that the respondent dislikes everything about the conference. And the second response would deliver positive sentiment, implying that the person liked everything about the event. But if we change the question to “What did you like about the conference?”, the sentiment behind these two answers will shift to the opposite polarity.
In order to capture the negative or positive sentiment in these replies, it is necessary to understand the context. However, the process of teaching a model how to understand it is not clear and straightforward.
People usually express sarcasm and irony using positive words. Machines may have hard times trying to understand the sentiment in these expressions without knowing the context. For example, on a traveling company’s website, we can find reviews answering the question “Did you enjoy traveling with us?”
Absolutely, the best travel agency ever!
Sure, the experience I got was unforgettable!
At first glance, these responses may look like positive comments, considering they contain such words as best and sure, which are usually marked as positive. However, these replies can also be interpreted as sarcastic and bear negative sentiment, and we can come up with multiple situations where it can be interpreted as such.
According to Guibon et al., there are three types of emojis: Western emojis, e.g., :0, containing one or two characters, more complex Eastern emojis, e.g. (°レ°), and the Unicode emoji characters. Analyzing emojis and characters is just as crucial as analyzing words and other speech components, especially when it comes to interpreting tweets. Emojis can also be broken down into tokens and whitelisted – this will help enhance sentiment analysis performance.
Western and Eastern emojis
Some other challenges are subjectivity and tone, human annotator accuracy, comparisons, etc. Even though machine learning is advancing rapidly, it will take much time and effort to resolve these issues.
Sentiment analysis can be applied in many spheres, including brand monitoring, market research, social media monitoring, etc. Let’s look at some of the most significant use cases.
Analyzing sentiment in blogs, forums, news articles, and other sources will help gauge the customer opinions and feelings surrounding your brand. You can align sentiment analysis with particular production and development cycles at your company, e.g., marketing campaigns, product releases, etc. Getting measurable statistics on customer satisfaction will assist in understanding how your brand representation develops over time and how it correlates with that of your competitors.
Apart from grasping the overall brand tendencies in the long-term perspective, you can also perform real-time sentiment analysis that allows you to identify possible reputational crises and take measures before they grow into more severe problems.
Sentiment analysis can be advantageous in any kind of market research, whether you’re studying your competition or exploring a new market. For example, you can study online reviews on your competitor’s new product, identify their strong suits and weak points and learn from them.
Following your brand and your competition on social media in real-time will help you reveal new trends as they pop up and adjust to the newly-appearing demands.
Sentiment analysis can enhance market research
Customers seek instant and stress-free interactions with brands. The way the companies provide their products and services is just as important as what they provide. In customer service, you can use customer sentiment analysis to arrange incoming client queries according to their urgency and topic and direct them to the respective departments. It makes communication with customers more efficient and ensures that the most time-sensitive matters are solved immediately.
As customers generate more and more reviews and comments online daily, it’s evident how important it is to process this data and draw conclusions promptly. Sentiment analysis provides an understanding of how your clients feel about your brand and product and how you can improve your services. Based on natural language processing and constantly progressing machine learning techniques, sentiment analysis serves multiple use cases, including brand monitoring and market research.
About the author
Maryia Stsiopkina is a Content Manager at Oxylabs. As her passion for writing was developing, she was writing either creepy detective stories or fairy tales for children at different points in time. Eventually, she found herself in the tech wonderland with numerous hidden corners to explore. In her spare time, she goes birdwatching with the binoculars (some people mistake it for stalking, which is why Maryia finds herself in an awkward situation sometimes), makes flower jewellery, and eats many pickles and green olives.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
Get the latest news from data gathering world
Scale up your business with Oxylabs®
GET IN TOUCH
Certified data centers and upstream providers
Connect with us