Understanding whether people feel positive or negative about a product, service, brand, or any subject -a.k.a. sentiment analysis- is vital for customer satisfaction and marketing departments. The expansion of the customer base of a product or service is directly proportional to customer satisfaction. Your consumers can turn into loyal customers only when they are satisfied with a product or service.
However, the size of the data required to make sound conclusions about how the target audience perceives a product or service makes it almost impossible to perform this analysis with just human power.
Let's take a closer look at how machine learning solves this problem and how it is used to understand sentiments.
Sentiment analysis may aim to detect feelings such as happiness, sadness, disappointment, anger, as well as understanding whether the view for the subject is positive or negative. In its purest form, sentiment analysis is "Trying to understand people's feelings and views towards a product, service or any subject using various methods.”
Among the broad practices of sentiment analysis are complaint management, crisis management, customer service, and reputation management through customer satisfaction surveys, as well as comments and conversations in the digital world.
Those who can benefit from sentiment analysis are numerous: The marketing and customer service departments of the companies, people conducting scientific research, political parties, and celebrities who want to learn how they are perceived by the public and develop a PR strategy accordingly, so forth and so on.
Interpreting the insights gained through sentiment analysis, we can understand whether a campaign is successful, customers are happy with the product, and customer relations departments are working effectively. Similarly, political parties regularly conduct sentiment analysis to see how the public perceives their discourses.
Thanks to the high capacity of computer processors and the power of artificial intelligence based on smart algorithms, machine learning allows us to conduct sentiment analysis in a much more practical, efficient, and effective way.
First of all, when we use machine learning technology, we save a lot of time and workforce. Let's assume we have 20,000 lines of data from our research we conducted to understand how our target audience perceived our company's last campaign. It would take days (if not weeks) to analyze this data line by line and make a sentiment classification such as positive and negative for each content. However, thanks to machine learning, it is possible to process this volume of data in less than half an hour.
On the other hand, instead of labeling the data line by line, let's assume that we want to automate the process by doing a keyword-based search in the Excel file. “If we search certain keywords and classify the line that contains this keyword accordingly, we can reduce our burden," we may think. However, when it comes to sentiment analysis, the structure of the language makes it difficult. For instance, if we automatically label any data that contains the word "Bravo!" as "positive," we would completely mislabel the comments that use this word to mock our campaign.
This is why products that use machine learning technology for the process are the best options. Systems that use machine learning, like Kimola Cognitive, automatically classify a high volume of data once the user labels a relatively small portion of it manually as a training set.
Besides, using machine learning technology, as in the case of "Bravo," provides a higher accuracy rate than keyword-based searches. When the system's smart algorithms see that the word "Bravo" is sometimes labeled "positive" and sometimes "negative," it starts to look for other variables to make a classification, such as the words used together with it, its location in the sentence, punctuation marks, etc.
The Sentiment Classification Model of Kimola enables you to get a more meaningful analysis by classifying your data as "positive" or "negative." It means less manual work and more time to take action.
The data was collected by using Kimola's Analytics platform from social media. It was chosen from different industries and topics, then labeled as "positive" and "negative." The primary step of preparing this training set was to determine if a sentence contains a "positive" or "negative" feeling. Also, attention was paid to divide the sentences with opposite sentiment and remove proper names such as brand names, company names, and celebrity names.
The training dataset contains thousands of rows of data. It was trained many times by using cross-validation and tested eight times in total. Besides, the development process of this model is still on, which means that its accuracy rate continues to increase continuously.
A large volume of data is needed to conduct reliable sentiment analysis. For this, you can try Kimola's Analytics product, which profiles thousands of people anonymously in real-time through their social media activity and enables you to discover trends for different target groups you have created. Kimola Analytics' ability to provide a wide range of data from demographic structure to areas of interest, from the most popular news sources to the most frequently used platforms for feedbacks, allows for in-depth analysis when reaching the insights.
Next comes the uploading of your database provided by Kimola Analytics or from other sources. You can easily upload your data to Kimola Cognitive, which does not require any technical knowledge to use, is entirely web-based, and has an interface that allows the data to be uploaded by just dragging and dropping.
To use the Sentiment Classification Model, you need to select this model instead of creating a new model when uploading your data to the system. Since Kimola already trained the set by marking thousands of rows of data, you won't need to train the system for it to classify your data based on sentiments. When you select the Sentiment Classification Model and upload your dataset, Kimola Cognitive creates a new column of "Sentiment" in the Excel file, analyzes the contents of each row, and labels them as "positive" or "negative." Thus, you can classify all your data quickly and practically.
After downloading the data classified as positive and negative, it is time to make sense of it. By examining the data, you can now see what aspects of your product or service people are satisfied with, and what about them causes negative sentiments. Once you determine the departments in charge of the areas of complaints, you can reach insights about the improvements you need to do. Likewise, you can now identify which features of your product or service are welcomed by the customers and which ones are not so that you can improve them accordingly.
Using machine learning technology, Kimola Cognitive's Sentiment Classification Model allows you to classify the sentiments in your high volume data quickly and with high accuracy.
If you’d like to try Kimola Cognitive and its Sentiment Classification Model for free, you can sign up here. To get more information about Cognitive, you can send your questions through this page, or you can request an appointment via Calendly.