Welcome to another blog-isode of Learn with me — a weekly educational series by Gauss Algorithmic. We take cutting-edge technological concepts and break them down into bite-sized pieces for everyday business people. Today we will cover sentiment analysis, a subcategory of NLP.
Welcome back to Learn with me, where we break down complicated tech topics so simply that even a marketer like me can understand. 🤷
Today I want to introduce sentiment analysis as a concept, without getting too bogged down in exactly how it works. We can delve deeper into the mechanics in a more advanced article, but there is immense value in just knowing what sentiment analysis is, and how it can help your business.
What is sentiment analysis?
Simply put, it means looking at text and making an educated guess as to how the writer felt when writing it.
Let’s try it ourselves
Sooo… how many stars? 🤔
You know that this is a one-star review. You also know it would be a negative-star review if it were an option. You know that the person is angry. You know that he or she had a bad customer experience.
How do you know all that?
Specifically, we know that “garbage” is not a word that you typically want to see multiple times in your review. 💡
Building a vocabulary
Sentiment Analysis algorithms can develop a vocabulary of words that might signify a positive or negative sentiment. It is possible to enter this vocabulary manually. ✍ However, it’s more common that a data scientist will provide only a partial list, which will be completed using machine learning.
For example, we can list “good,” “great,” and “wonderful” all as positive words. The algorithm can then look for patterns in language to find other synonyms that are used in a very similar way. This is how you can “seed” the algorithm with a partial vocabulary, and it can fill in the rest through machine learning. 🌱
So Sentiment Analysis is as simple as looking for keywords? We can just safely mark all posts containing “garbage” as negative, right?
Well, it’s not that simple. Context matters … and to provide that context, we can train a Sentiment Analysis with lots of data.
Data scientists feed the algorithm thousands of 1-star reviews, and it will be able to pick up patterns in language and word choice so that it will be able to recognize future 1-star reviews. 😠⭐ You can repeat the process with other ratings, and eventually the algorithm will be able to pretty effectively sort how satisfied someone is based on just the text.
Sentiment analysis can be used for many different tasks. Similar to the example above, companies can be alerted to 1-star reviews so that they can try to do some damage control. Similarly, 5-star reviews can also be brought to a company’s attention to reinforce whatever is working.
But that’s just scratching the surface.
Sentiment analysis can also be used for:
⚠️ Social media monitoring
💬 Customer support
⭐ Customer feedback
🔍 Brand monitoring
👨💼 Reputation management
📈 Much more!
Does your company spend lots of time analyzing reviews or other customer feedback? There’s a good chance that this process can be at least partially automated. Write to us, and we can tell you how!