Sentiment analysis has been gradually becoming more and more popular. Here’s how sentiment analysis has been trending over the last years in according to Google Trends:
We can definitely tell that with the development of e-commerce, SaaS tools and digital technologies, sentiment analysis is getting important.
What is Sentiment Analysis?
There’s a couple of definitions, be it by Wikipedia, by Brandwatch, or by Lexalytics. All in all, sentiment analysis boils down to one thing:
It’s the process of analyzing online pieces of writing to determine the emotional tone they carry.
In simple words, sentiment analysis is used to find the author’s attitude towards something. Some tools categorize pieces of writing as positive, neutral, or negative.
Here’s an example of a negative piece of writing because it contains hate.
Who Uses Sentiment Analysis?
Sentiment analysis and opinion mining have many applications ranging from ecommerce, marketing, to politics and any other research.
Let’s see how these areas make use of sentiment analysis.
Ecommerce and marketing
Sentiment analysis can be used in reputation management — to analyze web and social media mentions about a product, a service, a marketing campaign or a brand.
This is how companies can discover consumer attitudes towards them, their products, services, or marketing campaigns on discussion forums, review sites, Twitter, Instagram, Facebook and other publicly available sources.
Companies also do sentiment analysis to collect and analyze customer feedback.
These days, consumers use social media to share both their positive and negative experiences with brands. Sentiment analysis tools can detect both mentions conveying super positive pieces of content showing strengths of a product, or a service and negative mentions, bad reviews, or technical problems users write about online.
Some sentiment analysis tools like Brand24 collect and analyze pieces of writing including predefined keywords in real time. On seeing a negative mention, a company can quickly react and nip the problem in the bud before it escalates into a social media crisis.
Last year, United Airlines experienced an image crisis. Using a social media monitoring tool, we analyzed the sentiment of #UnitedAirlines hashtag. I wonder if they used a sentiment analysis tool at that time.
Here’s what popped out:
Some time ago UBER used social media monitoring and sentiment analysis tool to discover if users like the new version of their app.
It’s a pretty good case study that illustrates the use of sentiment analysis.
“At Uber, we use social listening on a daily basis, which allows us to understand how our users feel about the changes we’re implementing. As soon as we introduce a modification, we know which parts of it are greeted with enthusiasm, and which need more work. We’re happy that the new app was received so well because we’ve put a lot of work into it.”
Sentiment analysis is also used in politics. In 2012, the Obama administration used sentiment analysis tools to analyze the reception of policy announcement during 2012 presidential election.
During the last presidential election in the US, some organizations analyzed, for example how many negative mentions about particular candidates appeared in the media and news reports.
There have been at least a few academic papers examining sentiment analysis in relation to politics.
- Prediction of Indian election on the basis of Twitter sentiment analysis
- Political Data Science: Analyzing Trump, Clinton and Sanders Tweets and Sentiment
- Analysis of political sentiment in presidential elections in Egypt using Twitter data
Sentiment analysis and opinion mining finds application in any form of research and can extract sentiment from any piece of writing on the Web.
How sentiment analysis is done?
The science behind sentiment analysis is based on algorithms using natural language processing to to categorize pieces of writing as positive, neutral, or negative.
The algorithm is designed to identify positive and negative words, such as “fantastic”, “beautiful”, “disappointing”, “terrible”, etc.
This, however, isn’t always that easy.
Sentiment analysis challenges
Due to language complexity, sentiment analysis has to face at least a couple of issues.
One problem a sentiment analysis tool has to face is contrastive conjunctions — they happen when one piece of writing (a sentence) consists of two contradictory words (both positive and negative).
- Example sentence: “The weather was terrible, but the hike was amazing!”
Another big problem sentiment analysis algorithms face is named-entity recognition. Words in context have different meaning.
- Does “Everest” refer to the mountain or to the movie?
Also known as pronoun resolution, describes the problem of references within a sentence: what a pronoun, or a noun refers to.
- Example sentence: “We went to the theater and went for a dinner. It was awful.”
Is there any sentiment analysis tool detecting sarcasm? Please recommend one!
- Example sentence: “I’m so happy the plane is delayed.”
It just so happens that any language used online takes its own form. The economy of language and the Internet as a medium result in poor spelling, abbreviations, acronyms, lack of capitals and poor grammar. Analyzing such pieces of writing may cause problems for sentiment analysis algorithms.
How to do sentiment analysis?
Now let’s say what’s the best way to do sentiment analysis.
There’s a couple of sentiment analysis tools on the market. What’s interesting, most of social media monitoring tools can perform such an analysis.
One of the most affordable and effective solutions here is Brand24. It’s important to remember that sentiment analysis tools are still far from ideal and so is Brand24 — it fails to detect sarcasm and context nuances.
Still, you can extract valuable insights and make sense out of them. It’s worth trying as the trial account is completely free of any cost.
If you decide you want to try it out, here’s how to find your way inside the tool.
- Create a free account here. Then, provide keyword(s), or hashtag(s) you want to col mentions of from social media, discussion forums, websites, blogs, news sites and elsewhere.sIt can be, for example your company name.
- Next, the tool will take you the the main dashboard. In there, you can see all mentions of predefined keywords that appear in real time.`zAmong sources, volume chart and filters, there is Sentiment filter. By default, it’s set to display both negative and positive mentions. You can change it as you wish using the slider.
- In the Analysis tab, apart from other data, you can get a numerical and percentile summary of negative and positive mentions.
- To stay always on top of your mentions, you can set up email alerts that will inform you about, for example, negative mentions the moment they appear online.
Why would you do it all?
To sum up, from the marketing standpoint, sentiment analysis helps with:
- Identifying negative mentions about a business, a service, a company, a marketing campaign, an event in social media and on the Web
- Spotting angry customers on the verge of starting a social media crisis Analyzing how your customers react to product changes
- Spotting super happy users who, for example, are more likely to become your brand ambassadors
Do you have any experiences with with sentiment analysis tools?