"Sentiment analysis is the answer to any customer reaction or feedback about your brand."

The business world is making strides in providing customers with comfort and satisfaction services, due in part to the advent of new digital innovations. A multitude of digital platforms have emerged that offer a plethora of features to streamline business operations. One of the many developed features is sentiment analysis, which allows you to understand your customers' opinions about your brand.

These responses will generate data in the form of information that explains customer opinions or intentions about products, brands, or other topics. Like any other process, sentiment analysis also has its own process or work system in capturing data.

Then, how does sentiment analysis work system in retrieving data?

The sentiment analysis work system in retrieving data is divided into three steps. Reporting from ekrut.com, these steps include classification, evaluation, and visualization of results. The following is a description of the steps of the sentiment analysis process in retrieving data.

#1 Classify

This classification process is needed by the machine to classify data that is considered as an opinion of a text. There are three classifications in the sentiment analysis method that can be done, namely:

• Machine Learning: Features can recognize sentiment (a person's point of view) in a text. Machine learning methods are becoming more and more popular as they can be considered representative.
• Lexicon-based: Uses a variety of words assessed by a polarity score to find out what customers think about a topic. The advantage is that it does not require training data, but the disadvantage is that many words are not yet included in the lexicon.
• Blending: Combines machine learning and lexicon methods. Although rarely used, this method usually gives more promising results.

Read More: Maksimalkan Sentiment Analysis Ulasan Online dengan AI dari Ripple10

#2 Evaluation

Once the data has been classified, the next step is to use evaluation metrics such as precision, recall, f-score, and accuracy. This process also involves average measurements such as macro, micro, and weighted F1 scores to handle data that falls into two or more classifications. The metrics used are based on the classification balance of the data set. The general scheme is as follows:

• Dataset overview
• Pre-processing
• Tokenizer
• Stopword removal
• Transformation
• Classification
• Evaluation

#3 Data Visualization

The final step is data visualization. This is conducted using charts in accordance with the specific requirements of the company or individual utilizing the data. The majority of individuals employ techniques with which they are already familiar, such as graphs, histograms, or matrices. However, the ultimate outcome of the sentiment analysis process can vary considerably. 

The data may be presented alongside other domains, which is why data visualization techniques such as word clouds, interactive maps, and sparkline styles are also highly effective for displaying the results of the analysis.  An organized work system, as previously described, is an effective method for ensuring the smooth operation of the sentiment analysis process. This process is of significant importance to businesses, with many companies utilizing it to inform their marketing strategies.

One of the digital platforms, Ripple10 provides a Sentiment & Digital Health Index feature that offers a sentiment analysis process to monitor every customer sentiment on social media and prevent bad comments that can harm your brand image. If you are interested in Ripple10, please click this link.