Sentiment analysis

Sentiment analysis is a machine learning technique in social media and digital marketing that detects polarity. Detecting polarity (aka a positive or negative opinion) within a text can be found in places like a whole document, paragraph, sentence, or clause.

Understanding people’s emotions is essential for businesses since customers are able to express their thoughts and feelings more openly than ever before. By automatically analyzing customer feedback, from survey responses to social media conversations, brands are able to listen attentively to their customers, and tailor products and services to meet their needs.


Different types of sentiment analysis 

Sentiment analysis models focus on polarity (positive, negative, neutral) but also on feelings and emotions (angry, happy, sad, etc), and even on intentions (e.g. interested v. not interested).

Here are some of the most popular types of sentiment analysis:

  • Fine-grained Sentiment Analysis. If polarity precision is important to your business, you might consider expanding your polarity categories to include: Very positive, Positive, Neutral, Negative, and Very Negative. 
  • Emotion detection. This type of sentiment analysis aims at detecting emotions, like happiness, frustration, anger, sadness, and so on. Many emotion detection systems use lexicons (i.e. lists of words and the emotions they convey) or complex machine learning algorithms.
  • Aspect-based Sentiment Analysis. Usually, when analyzing sentiments of texts, let’s say product reviews, 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. 
  • 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. 

Benefits of sentiment analysis

  • Sorting data at scale. Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys? There’s just too much data to process manually. Sentiment analysis helps businesses process huge amounts of data in an efficient and cost-effective way. 
  • Real-Time Analysis 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 and gauge brand sentiment, so you can take action right away. 
  • Consistent criteria. 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.
  • Improve customer service. One of the benefits of sentiment analysis is being able to track the key messages from customers’ opinions and thoughts about a brand. This helps the customer service department to be aware of any related issues or problems.
  • Discovering new marketing strategies. With more data and information gathered through sentiment analysis, the organizations could develop an effective marketing strategy. The outcome from the strategies can be measured from the customers’ positive or negative key messages.
  • Improve media perceptions. Another benefit of Sentiment Analysis is to be able to track the understanding of the journalists, writers, columnists, market analysts, media researchers or independent contributors towards the company, be it the product, service, company values, human resources etc. This is crucial as any misinterpretation or negative connotation can lead to negative key messages which forms an undesirable perception.

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