Sentiment analysis is the automated process to analyze a text and interpret the sentiments behind it. Through machine learning, algorithms can classify statements as positive, negative, and neutral.
This process, also known as “opinion mining,” is often used by companies and brands as a strategy for social media monitoring to manage large amounts of data and gain consumer insights to learn more about customers and competitors.
What Is Sentiment Analysis Used For?
Sentiment analysis is used to analyze social media posts, tweets, and online product reviews, as a way to track opinions, reactions, and ultimately improve customer service and experience. It’s great for market research, brand and product reputation monitoring, and customer experience analysis.
These tools are not only used for analysis purposes, but also for predictions. Previous research suggests, for example, that positive sentiments may have an upward effect on stock prices.
The Definition of Sentiment Analysis
Millions of people around the globe today express their feelings on products and brands through the internet, whether it’s in a Yelp review, in a Twitter thread, or in a Facebook post. Companies have a strong interest in intercepting these online “conversations,” so that they can learn more about their customers and users, as well as the customers of their competitors in the market.
Given the magnitude of available data, it’s impossible for companies to manually search for reviews and comments, analyze them, and classify them as positive or negative. Thanks to sentiment analysis, this process can be automated, so that these insights can be gathered and evaluated through algorithms.
Of course, training machines to interpret sentiments written in textual form can be challenging, and that’s why not all companies that offer sentiment analysis solutions actually succeed at performing this task.
How Does Sentiment Analysis Work, Exactly?
Let’s say you own a restaurant and you scout for online reviews. Sentiment analysis can analyze them and quickly classify them as “positive,” “negative,” or “neutral.” For example, “The food was delicious!” can be easily classified as strongly positive, while “The service sucks” will be identified as a strongly negative comment. Thanks to a “sentiment library,” a sentiment analysis tool can easily identify nouns, verbs, adjectives, and adverbs in these texts and recognize that “delicious” is an indicator of a positive reaction, while “sucks” is an indicator of a negative one.
If all reviews were so straightforward, it would be quite easy to train a machine to do the job. However, most reviews are more subtle and nuanced.
For instance, one reviewer may say, “The food was good, but the music was too loud.” Another might call the restaurant “Not bad.”
Sentiment analysis usually assesses the “score” of a text, placing it on a spectrum of attitudes that goes between +1 (totally positive) and -1 (totally negative). This way, machines are able to distinguish between an enthusiastic comment and a milder, still positive one.
For instance, let’s say your brand has recently put out a new commercial that has been played on television. You can use social media listening to see if people on Twitter have been commenting on your new ad. A sentiment analysis tool will be able to distinguish between different scores of positivity in the two following comments: (1) “I’m obsessed with this new commercial!” and (2) “That’s a cute commercial.” While both of them are positive, the first one will receive a higher score, as it’s clearly more enthusiastic.
Some Of The Challenges In Sentiment Analysis
As we mentioned earlier, a text can be quite hard for a machine to dissect and interpret.
A user may write: “We had to wait 45 minutes to get a table. Great!” To a human being, it’s clear that the adjective “Great!” is used in a sarcastic way. How do we know it? Because of context. We read the previous sentence, which talks about a long wait time, and we understand that the comment is not positive at all. A good sentiment analysis tool has to be able to detect sarcasm from the broader context, otherwise you’ll end up getting inaccurate data about your brand at the end of the analysis.
Another issue has to do with nuance. The comment “The movie was not bad” is literally saying that the movie was not bad, maybe even good; but it’s also implying that the expectations regarding this movie were so low that the movie is not as bad as one would have expected it to be. This is called “negator.”
Also “intensifiers” can be challenging for sentiment analysis. A user who writes “The company’s comment on this issue was pretty good,” creates a nuance that would not be there if we read the same sentence without the word “pretty.”
In conclusion, it’s important not to rely on very basic and simple sentiment analysis tools, which are definitely not going to capture the complexity of human sentiments expressed through text.
Steps in Sentiment Analysis
As we dig further in understanding this powerful marketing and branding tool, let’s look at the pipeline of steps usually applied in sentiment analysis.
In this pipeline sample, we’ll consider sentiment analysis for a given company or brand.
Step1: Data gathering- First of all, we need the data that we will later analyze. We can gather data from social media, namely Twitter, using scraping tools, APIs, customers’ data feed, and so on. We can also gather data from user reviews on services like Google and Yelp. We’ll be looking for all mentions of the company or brand over a specific period of time. This practice is very common in all forms of social media listening.
Step 2: Text cleaning- Text cleaning tools will allow us to process the data and prepare it for the analysis by removing stopwords (a, and, or, but, how, what…), punctuation (commas, periods…), and checking for stemming. These tools will allow us to “clean” or “strip” the texts from anything that might be irrelevant to the analysis.
Step 3: Sentiment analysis (or opinion mining)- At this point, we can use our sentiment analysis algorithms to analyze the data that we have gathered. As we saw earlier, the most common classification is the spectrum between “positive” and “negative.” However, more refined tools may also identify more complex sentiments such as anger, sadness, and so on. The algorithms will use a sentiment library to identify opinions and classify them.
Step 4: Understanding the results- At the end of the process, we should be able to see the data grouped into major categories. We should be able to see if we have more positive, neutral, or negative reactions. Having each sentiment tagged with its original date is particularly important, as a timeline will show us if we had “peaks” (surges of positive sentiments) or “valleys” (surges of negative sentiments) in specific moments in time. We might therefore be able to find correlations between something that happened on a specific date and a surge of opinions regarding our brand.
While we might identify a peak or a valley while performing sentiment analysis, the opposite might happen—we might notice a surge in mentions on Twitter and we therefore might use sentiment analysis to understand the users’ reactions.
For example, an airline might notice a surge in mentions on Twitter due to some viral content regarding the airline. Given the magnitude of data on the social media network, the company might use data gathering to collect all those mentions; it will then perform sentiment analysis to study the reaction of the public to the viral content. Here’s why sentiment analysis is so important: Understanding whether the reaction is positive or negative can be useful for the company to decide to pursue one of the following actions:
- If the reaction is positive, the airline might want to capitalize on the moment to push a new commercial campaign or pitch the content to the news media.
- If the reaction is negative, the airline might want to prevent a brand crisis by taking action or publishing a statement as soon as possible.
How Revuze Performs Sentiment Analysis
At Revuze, a premier big data consumer analytics firm, we personalize automated sentiment analysis to maximize its accuracy and success rate.
We do it through “local models,” which allow us to adapt our technology to the peculiarities of each case study or client. Within just a few days, we can generate local dictionaries and models with a 90% accuracy. Compared to other tools, which take months to develop local dictionaries, we ensure that our clients can benefit from valuable consumer insights significantly faster.
Here’s how it works: Revuze’s AI algorithms extract many unique topics, ranging from high-level ones (like user satisfaction and price) to granular topics (such as “softness” for toilet paper or “moisturizing strip” for disposable razors). Instead of limiting ourselves to only 8-15 generic topics, we analyze 40-80 topics that are highly specific to each business or product we work with.
The truth is that, when you try to understand consumer sentiment around a certain product feature, you cannot afford to use a sentiment analysis tool that is limited to generic topics. Personalization is key.
With Revuze, you can get a first look at the insights of modern consumer usage, which are typically hidden.
Schedule your demo with Revuze today.