How to Do Sentiment Analysis in 4 Steps (With Examples)

Learn what sentiment analysis is and how to use it with ease.

Knowing how your customers feel about your product is important. It helps your company understand where you’re succeeding, where you need to improve, and how to better reach new customers. Sentiment analysis can help achieve these goals. It’s used to analyze customer feedback from various channels, like survey responses and product reviews.

Creating your own sentiment analysis is invaluable. Read on to learn more about the process and review our step-by-step guide.

What is sentiment analysis?

Sentiment analysis is powered by artificial intelligence. The software combs through reviews, videos, comments, social media posts, surveys, and emails, among other sources, to find out what kind of language customers are using to describe their products. Because this usually involves massive amounts of data, companies use AI to speed up the process.

The AI will search through each dataset and analyze the sentiment described by certain words, phrases, or text. Each item is given a sentiment score, which is typically on a scale of -100 to 100, where zero is considered neutral. This allows you to search for a certain key word or phrase, like “price,” and determine whether the surrounding sentiment is positive, neutral, or negative.

How to do your own sentiment data analysis

Here’s an overview of the steps involved in performing sentiment analysis.

Data collection

This crucial step ensures that you have quality data available. Data must be gathered and annotated to produce good results. The data you collect can be uploaded through a live API, which allows you to glean publicly available data from platforms like Amazon reviews, Facebook, Twitter, or open-source data repositories like Kaggle. Data can also be manually uploaded to the sentiment analysis API using a .csv file.

Data processing

Next, the data needs to be processed. This step varies, depending on the type of medium it is, like images, text, audio, and video. This can include:

  • Transcriptions: Audio and video files need to be transcribed through speech-to-text software.
  • Image text: If there are any images with text in the dataset, your AI software can recognize and capture that text using optical character recognition.
  • Captions: Any captions in a video are also extracted, so they can be analyzed for the key words or phrases you have chosen.
  • Logo recognition: AI can be trained to recognize any logos that appear in images or video, whether the logo was intentionally added to the video or it appears on incidental items, like clothing, mugs, and more.
  • Emojis: Even emojis can be extracted from datasets. Since people often use emojis to convey emotion, this can provide very helpful insight.

Data analysis

Next, the data is analyzed. This includes:

  • Training the model: First, you must teach the platform how to classify your data. Using a classified and labeled sentiment analysis of datasets requires manual labeling and pre-processing. This trains the model to recognize correctly and incorrectly classified data. If you will be using multilingual data processing, you’ll need separate datasets for each language.
  • Tags: Next, you’ll create custom tags for aspects and themes in the data. This could include product names, mentions of your brand, and more. After the model is trained, the custom tags allow the AI to separate the text based on the tags.
  • Topic classification: Topic classifying associates a theme with text. For example, if you’re analyzing how people feel about your department store, you might train the model to associate “television” and “TV” with “electronics.”
  • Analyzing for sentiment: Finally, the aspects and themes are isolated and reviewed for the sentiment. Then they’re assigned a sentiment score. The AI aggregates the score for each aspect and theme and gives an average score to denote the overall sentiment.

Data visualization

After the data is analyzed, it is then turned into graphs and charts. These reports are often distributed to your teams. Data visualization allows you to make complex data analysis into easy-to-understand images. It’s particularly helpful for identifying broad areas of focus.

For instance, if your customer service scores are considerably lower than the other themes or aspects you analyzed, that will stand out. This makes it simple to review the data and create tasks based on your results.

Benefits of sentiment analysis tracking

There are four main benefits of sentiment analysis. First, it allows you to remove human bias from the analysis. Instead of trying to determine whether a statement is positive or negative yourself, the AI will sort through each sentiment and separate them. That allows for a consistent analysis and prevents certain statements from being weighted improperly.

Second, sentiment analysis can process data at scale. This is far more efficient and cost-effective than having workers comb through datasets. In turn, the powerful software saves your company time. It takes just minutes to analyze hundreds of megabytes of text, which frees you up to focus on other tasks.

Finally, sentiment analysis allows your company to get real-time analysis and insight. The fast analysis can get through vast amounts of data very quickly, so your company can monitor trends and feedback in real time. This allows you to keep your finger on the pulse of customer feedback, alerting you to problems and helping you make decisions about how different products are resonating with customers.

In short, sentiment analysis is a powerful and efficient tool for businesses, from product development to customer service.

Gather sentiment analytics data with Voiceform

Sentiment analysis starts with good data. Voiceform allows you to collect feedback from clients, colleagues, and more in multiple ways. Respondents can use text or audio to deliver their feedback. This encourages people who might not ordinarily provide feedback through text to share their opinions.

Once you’ve gathered data with Voiceform, use the results to power your sentiment analysis. Voiceform automates the entire process, from data collection and processing to analysis and visualization through our results dashboard. We’ll do the heavy lifting for you!

Together, these tools can provide invaluable insight into your company’s performance. Learn more about Voiceform by scheduling a free demo today.

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