Sentiment Analysis Data Pipeline: What, Why, and How?
Understand sentiment analysis and why is it important. Learn about the different types of sentiment analysis along with real-life examples.
Join the DZone community and get the full member experience.Join For Free
What Is Sentiment Analysis?
In just 4 years, a whopping 6 billion users — that’s half of the world’s population — will be active on social media. And if you’re curious to know the time we spend on social media, it is a jaw-dropping 147 minutes daily.
Any place where people spend so much time of their day is important from a business perspective. Many businesses realize this and invest heavily in analyzing data from social media. In most cases, businesses are concerned about the sentiments on social media regarding their brand. It helps them gain insights into the kind of sentiments that social media users have regarding their brand.
Types of Sentiment Analysis
Sentiment analysis focuses on the polarity of a text (positive, negative, neutral) but it also goes beyond polarity to detect specific feelings and emotions (angry, happy, sad, and so on), urgency (urgent, not urgent) and even intentions (interested vs. not interested).
Depending on how you want to interpret customer feedback and queries, you can define and tailor your categories to meet your sentiment analysis needs. In the meantime, here are some of the most popular types of sentiment analysis:
Graded Sentiment Analysis
If polarity precision is important to your business, you might consider expanding your polarity categories to include different levels of positive and negative:
- Very positive
- Very negative
This is usually referred to as graded or fine-grained sentiment analysis, and could be used to interpret 5-star ratings in a review, for example:
- Very positive = 5 stars
- Very negative = 1 star
Emotion detection sentiment analysis allows you to go beyond polarity to detect emotions, like happiness, frustration, anger, and sadness.
Many emotion detection systems use lexicons (i.e., lists of words and the emotions they convey) or complex machine learning algorithms.
One of the downsides of using lexicons is that people express emotions in different ways. Some words that typically express anger, like "bad" or "kill" (e.g., "your product is so bad" or "your customer support is killing me") might also express happiness (e.g., "this is badass" or "you are killing it").
Aspect-Based Sentiment Analysis
Usually, when analyzing sentiments of texts you’ll want to know which particular aspects or features people are mentioning in a positive, neutral, or negative way.
That's where aspect-based sentiment analysis can help. For example, in this product review: "The battery life of this camera is too short," an aspect-based classifier would be able to determine that the sentence expresses a negative opinion about the battery life of the product in question.
Multilingual Sentiment Analysis
Multilingual sentiment analysis can be difficult. It involves a lot of preprocessing and resources. Most of these resources are available online (e.g., sentiment lexicons), while others need to be created (e.g., translated corpora or noise detection algorithms), but you’ll need to know how to code to use them.
Alternatively, you could detect the language in texts automatically with a language classifier, then train a custom sentiment analysis model to classify texts in the language of your choice.
Why Is Sentiment Analysis Important?
Since humans express their thoughts and feelings more openly than ever before, sentiment analysis is fast becoming an essential tool to monitor and understand sentiment in all types of data.
Automatically analyzing customer feedback, such as opinions in survey responses and social media conversations, allows brands to learn what makes customers happy or frustrated, so that they can tailor products and services to meet their customers’ needs.
For example, using sentiment analysis to automatically analyze 4,000+ open-ended responses in your customer satisfaction surveys could help you discover why customers are happy or unhappy at each stage of the customer journey.
Maybe you want to track brand sentiment so you can detect disgruntled customers immediately and respond as soon as possible. Maybe you want to compare sentiment from one quarter to the next to see if you need to take action. Then you could dig deeper into your qualitative data to see why sentiment is falling or rising.
The overall benefits of sentiment analysis include:
Understanding Your Audience Better
You may better grasp the needs of your brand’s audience by using social media listening.
For instance, a current client could tweet about how much they enjoy your product. Or you could hear someone talking about a problem that your product or service might solve.
You can utilize these insights to enhance your product and customer satisfaction.
Sorting Data at Scale
Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys? There’s just too much business data to process manually. Sentiment analysis helps businesses process huge amounts of unstructured data in an efficient and cost-effective way.
Sentiment analysis can identify critical issues in real time. For example, is a PR crisis on social media escalating? Is an angry customer about to churn? Sentiment analysis models can help you immediately identify these kinds of situations, so you can take action right away.
It’s estimated that people only agree around 60-65% of the time when determining the sentiment of a particular text. Tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs.
By using a centralized sentiment analysis system, companies can apply the same criteria to all of their data, helping them improve accuracy and gain better insights.
The applications of sentiment analysis are endless. So, to help you understand how sentiment analysis could benefit your business, let’s take a look at some examples of texts that you could analyze using sentiment analysis.
Sentiment Analysis Examples
To understand the goal and challenges of sentiment analysis, here are some examples:
Basic examples of sentiment analysis data:
- Netflix has the best selection of films.
- Hulu has a great UI.
- I dislike the new crime series.
- I hate waiting for the next series to come out.
More challenging examples of sentiment analysis:
- I do not dislike horror movies. (a phrase with negation)
- Disliking horror movies is not uncommon. (negation, inverted word order)
- Sometimes I really hate the show. (adverbial modifies the sentiment)
- I love having to wait two months for the next series to come out! (sarcasm)
- The final episode was surprising with a terrible twist at the end. (negative term used in a positive way)
- The film was easy to watch, but I would not recommend it to my friends. (difficult to categorize)
- I LOL’d at the end of the cake scene. (often hard to understand new terms)
Sentiment analytics can help businesses like yours stay ahead of the competition and make data-driven decisions to improve their bottom line. But to tap into the power of this game-changing strategy, you will need a reliable data engineering partner to build a robust pipeline. Did I miss on any of the important points? Let me know in the comments below.
Published at DZone with permission of Hiren Dhaduk. See the original article here.
Opinions expressed by DZone contributors are their own.